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I was initially a Computer Programmer and stayed in the software development field for more than 15 years. Now, it's been 30 years that I am in IT field.I am very simple, down-to-earth and would like to get in touch with more and more professionals and experts. I respect work and know that even a small piece of work requires a dedication and good effort.<br /><span class="u-fw700">Supervisors: </span>Huang Chuanhe<br /><span class="u-fw700">Phone: </span>+9779840050150<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="suggested-academics-container"><div class="suggested-academics--header"><h3 class="ds2-5-heading-sans-serif-xs">Related Authors</h3></div><ul class="suggested-user-card-list" data-nosnippet="true"><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a data-nosnippet="" href="https://unizg.academia.edu/AndrejDujella"><img class="profile-avatar u-positionAbsolute" alt="Andrej Dujella related author profile picture" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" 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data-dom-id="Pill-react-component-7d752281-d161-4562-8d6c-c2347520e8d2"></div> <div id="Pill-react-component-7d752281-d161-4562-8d6c-c2347520e8d2"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="371695" href="https://www.academia.edu/Documents/in/Machine_Learning"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Machine Learning"]}" data-trace="false" data-dom-id="Pill-react-component-70271099-e26a-427a-ba8e-d623819c22dd"></div> <div id="Pill-react-component-70271099-e26a-427a-ba8e-d623819c22dd"></div> </a></div></div><div class="external-links-container"><ul class="profile-links new-profile js-UserInfo-social"><li class="profile-profiles js-social-profiles-container"><i class="fa fa-spin fa-spinner"></i></li></ul></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="nav-container backbone-profile-documents-nav hidden-xs"><ul class="nav-tablist" role="tablist"><li class="nav-chip active" role="presentation"><a data-section-name="" data-toggle="tab" href="#all" role="tab">all</a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Papers" data-toggle="tab" href="#papers" role="tab" title="Papers"><span>34</span> <span class="ds2-5-body-sm-bold">Papers</span></a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Books" data-toggle="tab" href="#books" role="tab" title="Books"><span>1</span> <span class="ds2-5-body-sm-bold">Books</span></a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Thesis-Chapters" data-toggle="tab" href="#thesischapters" role="tab" title="Thesis Chapters"><span>1</span> <span class="ds2-5-body-sm-bold">Thesis Chapters</span></a></li></ul></div><div class="divider ds-divider-16" style="margin: 0px;"></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Roshan Chitrakar</h3></div><div class="js-work-strip profile--work_container" data-work-id="79858816"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/79858816/Intrusion_Detection_Based_on_PCA_with_Improved_K_Means"><img alt="Research paper thumbnail of Intrusion Detection Based on PCA with Improved K-Means" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Intrusion Detection Based on PCA with Improved K-Means</div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/pralhadchapagain">pralhad chapagain</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://ncit.academia.edu/RoshanChitrakar">Roshan Chitrakar</a></span></div><div class="wp-workCard_item"><span>Lecture notes in electrical engineering</span><span>, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79858816"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79858816"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79858816; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79858816]").text(description); $(".js-view-count[data-work-id=79858816]").attr('title', description).tooltip(); }); });</script></span></span><span><span 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alt="Research paper thumbnail of Peer-Review Statements" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Peer-Review Statements</div><div class="wp-workCard_item"><span>Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research</span><span>, 2024</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125390915"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item 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Chitrakar","url":"https://ncit.academia.edu/RoshanChitrakar"},"attachments":[],"research_interests":[],"urls":[{"id":45527281,"url":"https://doi.org/10.2991/978-2-38476-230-9_1"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-125390915-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="125390914"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/125390914/Turbocharging_Hadoop_Fair_Scheduler_using_Dynamic_Job_Grouping_in_Multi_Job_Workloads"><img alt="Research paper thumbnail of Turbocharging Hadoop Fair Scheduler using Dynamic Job Grouping in Multi-Job Workloads" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Turbocharging Hadoop Fair Scheduler using Dynamic Job Grouping in Multi-Job Workloads</div><div class="wp-workCard_item"><span>Authorea (Authorea)</span><span>, Feb 9, 2024</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125390914"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="125390914"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125390914; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=125390914]").text(description); $(".js-view-count[data-work-id=125390914]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 125390914; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='125390914']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=125390914]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":125390914,"title":"Turbocharging Hadoop Fair Scheduler using Dynamic Job Grouping in Multi-Job Workloads","translated_title":"","metadata":{"publication_date":{"day":9,"month":2,"year":2024,"errors":{}},"publication_name":"Authorea 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Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":440,"name":"Distributed Computing","url":"https://www.academia.edu/Documents/in/Distributed_Computing"},{"id":165579,"name":"Interdependence","url":"https://www.academia.edu/Documents/in/Interdependence"},{"id":172784,"name":"Workload","url":"https://www.academia.edu/Documents/in/Workload"},{"id":2221385,"name":"Job Queue","url":"https://www.academia.edu/Documents/in/Job_Queue"}],"urls":[{"id":45527280,"url":"https://www.techrxiv.org/doi/pdf/10.22541/au.170750113.39811069/v2"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-125390914-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="125390913"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/125390913/Measurement_of_Kernel_Preemption_Time_of_Real_Time_Operating_Systems"><img alt="Research paper thumbnail of Measurement of Kernel Preemption Time of Real Time Operating Systems" class="work-thumbnail" src="https://attachments.academia-assets.com/119443361/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/125390913/Measurement_of_Kernel_Preemption_Time_of_Real_Time_Operating_Systems">Measurement of Kernel Preemption Time of Real Time Operating Systems</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In a Real Time System, when a high priority task is scheduled, it preempts ongoing lower priority...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In a Real Time System, when a high priority task is scheduled, it preempts ongoing lower priority tasks and sometimes it preempts even the kernel too. In normal cases, the time needed for the preemption is too small and hence is not considered in task scheduling. But, in some mission critical hard real time systems, even a small fraction of a second should be taken into account as it could affect the deadline. This research work proposes two models -embedded model and software model, among which the latter has been used for the experiment that calculates the kernel preemption time. The hardware model consists of a triangular wave generator called TRIANG that takes timer values from real time tasks and sends outputs to a comparator module. Finally, the comparator compares the time differences, and hence, calculates the kernel preemption time. Whereas the software model uses the RT-Linux architecture in which device drivers are used instead of hardware components. A high precision timer clock is also used to measure the task release times. The results from the experiments show that the release time jitter due to the kernel preemption is found to be fluctuating in the range of -94 and +93 microseconds in 3% of the cases. So, the kernel preemption time for the hard and mission critical real time systems is taken to be less than 99 microseconds. Whereas 85% of the cases have release time jitters of single digit only. So, the kernel preemption time in average case is considered to be less than 9 microseconds.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-125390913-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-125390913-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708414/figure-1-embedded-model-of-the-kernel-preemption-measurement"><img alt="Figure 1: Embedded Model of the Kernel Preemption Measurement. There is a device or a software simulator called the TRIANG that generates a triangular sweep wave [30]. Besides, ‘n’ numbers of tasks with real time priorities are scheduled. Both the waves and the jobs share the central memory as shown in Figure 1. Obviously, the timer is associated with all the tasks and also to the TRIANG. The system calls provided by the RTOS are used to activate timer C.... kt " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708417/figure-2-the-embedded-model-is-altered-bit-in-order-to"><img alt="The Embedded model is altered a bit in order to obtain the Software model as show in Figure 2. The triangular wave generator is absent here and the comparator is replaced with a software tool to which | have given the name “Time Measuring Tool” that could be one of the following: " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708421/figure-3-the-key-concept-of-the-model-is-that-it-measures"><img alt="The key concept of the model is that it measures the intervals of two consecutive periodic releases of the real time task - giving rise to the measurement of the release time jitters due to the kernel preemption delay. Essentially, the measurement is done for a several thousand releases of the task. The worst time is considered to be the required measurement. Between the two models, the software model was chosen for the experiment while another model is left as a separate research work. RTLinux was selected as the OS kernel due to its superiority among others [14]. " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708423/figure-4-all-the-collected-data-are-shown-with-the-help-of"><img alt="All the collected data are shown with the help of a line chart. A number of line charts are prepared with increasing number of data collected. All the line charts have data series of “preemption disabled” case also because of the intent of showing the interrupt blocking time as well. ll the collected data are shown with the help of a line chart. A number of line charts are prepared with increasing number of data Measurement of Kernel Preemption Time of Real Time Operating Systems " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708425/figure-1-the-central-ideal-straight-line-is-the-defined"><img alt="The central ideal straight line is the defined period showing the value of 10000 microseconds at each period. The dark line touching above the ideal line is the preemptible case and the most curved line represents the non-preemptible case. The graph shown by Figure 1 shows the 100 collected data. In here, the preemptible case has a uniformity and very less fluctuation at the ideal line at 10000. " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/figure_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708427/figure-6-discussion-measurement-of-kernel-preemption-time-of"><img alt="Discussion Measurement of Kernel Preemption Time of Real Time Operating Systems " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/figure_006.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708428/table-1-introduction-measurement-of-kernel-preemption-time"><img alt="Introduction Measurement of Kernel Preemption Time of Real Time Operating Systems A Real Time System is in general categorized into three types- Hard, Soft and Firm Real Time System according to the usefulness unction on the basis of their timing constraints i.e. deadline [1]. Hard RTS is subject to crash or may cause physical hazards upon 10t meeting the deadline. A mission critical system, for example, always has a hard deadline [2]. In order to schedule the tasks of the system, all parameters including temporal and resource parameters for task preemption should be known well in advance [3]. The orrectness of the timing measurements is vital in this regard because task should be executed exactly at the one time (considering the ask execution time and delays due to other overheads) in order to meet the deadline at the given time [4]. " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708429/table-4-release-time-jitters-categorically-broken-down-hence"><img alt="Table 4: Release Time Jitters categorically broken-down. Hence, it would be worth looking at the categorical distribution of the jitters measured. All jitters are grouped into 3 (three) cate- ories: - No jitters at all (0), One digit number (0-9) and two-digit number (10-99). Here is Table 3 showing the occurrences of jitters nd their percentage with respect the data collected. " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/table_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708430/table-3-categorical-distribution-of-release-time-jitters-it"><img alt="Table 3: Categorical distribution of release time jitters. It is interesting observation that the occurrence of the min and max jitters are only once each - contributing only 0.04% of the total. Table 2: Min and Max data observed from the experiment. " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/table_003.jpg" /></a></figure></div><div class="next-slide-container js-next-button-container"><button aria-label="Next" class="carousel-navigation-button js-profile-work-125390913-figures-next"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_forward_ios</span></button></div></div></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="00297707cd1cddada30de6a8880eaeb4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119443361,"asset_id":125390913,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119443361/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125390913"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="125390913"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125390913; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=125390913]").text(description); $(".js-view-count[data-work-id=125390913]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 125390913; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='125390913']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "00297707cd1cddada30de6a8880eaeb4" } } $('.js-work-strip[data-work-id=125390913]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":125390913,"title":"Measurement of Kernel Preemption Time of Real Time Operating Systems","translated_title":"","metadata":{"ai_title_tag":"Kernel Preemption Time in Real Time Systems","grobid_abstract":"In a Real Time System, when a high priority task is scheduled, it preempts ongoing lower priority tasks and sometimes it preempts even the kernel too. In normal cases, the time needed for the preemption is too small and hence is not considered in task scheduling. But, in some mission critical hard real time systems, even a small fraction of a second should be taken into account as it could affect the deadline. This research work proposes two models -embedded model and software model, among which the latter has been used for the experiment that calculates the kernel preemption time. The hardware model consists of a triangular wave generator called TRIANG that takes timer values from real time tasks and sends outputs to a comparator module. Finally, the comparator compares the time differences, and hence, calculates the kernel preemption time. Whereas the software model uses the RT-Linux architecture in which device drivers are used instead of hardware components. A high precision timer clock is also used to measure the task release times. The results from the experiments show that the release time jitter due to the kernel preemption is found to be fluctuating in the range of -94 and +93 microseconds in 3% of the cases. So, the kernel preemption time for the hard and mission critical real time systems is taken to be less than 99 microseconds. Whereas 85% of the cases have release time jitters of single digit only. So, the kernel preemption time in average case is considered to be less than 9 microseconds.","publication_date":{"day":29,"month":11,"year":2023,"errors":{}},"grobid_abstract_attachment_id":119443361},"translated_abstract":null,"internal_url":"https://www.academia.edu/125390913/Measurement_of_Kernel_Preemption_Time_of_Real_Time_Operating_Systems","translated_internal_url":"","created_at":"2024-11-08T19:45:30.354-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":119443361,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/119443361/thumbnails/1.jpg","file_name":"mcet.05.pdf","download_url":"https://www.academia.edu/attachments/119443361/download_file","bulk_download_file_name":"Measurement_of_Kernel_Preemption_Time_of.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/119443361/mcet.05-libre.pdf?1731128694=\u0026response-content-disposition=attachment%3B+filename%3DMeasurement_of_Kernel_Preemption_Time_of.pdf\u0026Expires=1743492636\u0026Signature=gPCdJfHs3o7T6fpbZXwwbybYa180CK-4DbAxVia6gHHtkFezxN39-AfgSaVUh9URCxy2Lvgglz6iGdm7MqF4YymNd1j38QeDH-BZNxLPmYVDmEBb0OsBIJVf6FomDc~Csrao44yhMz1NsxZ42XNsvfxYp8ShCkwbkzVKCwz8Br0wftEpKxLA94xA0hQN335dctACy3O9jRUze38LWTtebMiyqhFLEdBb8jAjD4gvCiNO2SdxG-lsDHvLF6--LCbjGgYcR8uEUoP8H7E5gtWMf2WAbkuYGfaTsrkFS3QpJQuIWq9dHUozHheCzY742UO35auGGT0uPIsi0ZCUOm~eew__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Measurement_of_Kernel_Preemption_Time_of_Real_Time_Operating_Systems","translated_slug":"","page_count":12,"language":"en","content_type":"Work","summary":"In a Real Time System, when a high priority task is scheduled, it preempts ongoing lower priority tasks and sometimes it preempts even the kernel too. In normal cases, the time needed for the preemption is too small and hence is not considered in task scheduling. But, in some mission critical hard real time systems, even a small fraction of a second should be taken into account as it could affect the deadline. This research work proposes two models -embedded model and software model, among which the latter has been used for the experiment that calculates the kernel preemption time. The hardware model consists of a triangular wave generator called TRIANG that takes timer values from real time tasks and sends outputs to a comparator module. Finally, the comparator compares the time differences, and hence, calculates the kernel preemption time. Whereas the software model uses the RT-Linux architecture in which device drivers are used instead of hardware components. A high precision timer clock is also used to measure the task release times. The results from the experiments show that the release time jitter due to the kernel preemption is found to be fluctuating in the range of -94 and +93 microseconds in 3% of the cases. So, the kernel preemption time for the hard and mission critical real time systems is taken to be less than 99 microseconds. Whereas 85% of the cases have release time jitters of single digit only. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (true) { Aedu.setUpFigureCarousel('profile-work-125390913-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="109494623"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/109494623/Computational_Social_Science"><img alt="Research paper thumbnail of Computational Social Science" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Computational Social Science</div><div class="wp-workCard_item"><span>Routledge eBooks</span><span>, Jun 9, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494623"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494623"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494623; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-109494623-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="109494622"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109494622/Comparison_of_Data_MigrationTechniques_from_SQL_Databaseto_NoSQL_Database"><img alt="Research paper thumbnail of Comparison of Data MigrationTechniques from SQL Databaseto NoSQL Database" class="work-thumbnail" src="https://attachments.academia-assets.com/107603551/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109494622/Comparison_of_Data_MigrationTechniques_from_SQL_Databaseto_NoSQL_Database">Comparison of Data MigrationTechniques from SQL Databaseto NoSQL Database</a></div><div class="wp-workCard_item"><span>Journal of computer engineering & information technology</span><span>, Dec 28, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">With rapid and multi-dimensional growth of data, Relational Database Management System (RDBMS) ha...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">With rapid and multi-dimensional growth of data, Relational Database Management System (RDBMS) having Structured Query Language (SQL) support is facing difficulties in managing huge data due to lack of dynamic data model, performance and scalability issues etc. NoSQL database addresses these issues by providing the features that SQL database lacks. So, many organizations are migrating from SQL to NoSQL. RDBMS database deals with structured data and NoSQL database with structured, unstructured and semi-structured data. As the continuous development of applications is taking place, a huge volume of data collected has already been taken for architectural migration from SQL database to NoSQL database. Since NoSQL is emerging and evolving technology in the field of database management and because of increased maturity of NoSQL database technology, many applications have already switched to NoSQL so that extracting information from big data. This study discusses, analyzes and compares 7 (seven) different techniques of data migration from SQL database to NoSQL database. The migration is performed by using appropriated tools / frameworks available for each technique and the results are evaluated, analyzed and validated using a system tool called SysGauge. The parameters used for the analysis and the comparison are Speed, Execution Time, Maximum CPU Usage and Maximum Memory Usage. At the end of the entire work, the most efficient techniques have been recommended.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0bb9a09f9da325b48c9fbac6b9c63496" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107603551,"asset_id":109494622,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107603551/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494622"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494622"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494622; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109494622]").text(description); $(".js-view-count[data-work-id=109494622]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109494622; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109494622']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "0bb9a09f9da325b48c9fbac6b9c63496" } } $('.js-work-strip[data-work-id=109494622]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109494622,"title":"Comparison of Data MigrationTechniques from SQL Databaseto NoSQL Database","translated_title":"","metadata":{"publisher":"OMICS Publishing Group","ai_title_tag":"Comparative Analysis of SQL to NoSQL Data Migration Techniques","grobid_abstract":"With rapid and multi-dimensional growth of data, Relational Database Management System (RDBMS) having Structured Query Language (SQL) support is facing difficulties in managing huge data due to lack of dynamic data model, performance and scalability issues etc. NoSQL database addresses these issues by providing the features that SQL database lacks. So, many organizations are migrating from SQL to NoSQL. RDBMS database deals with structured data and NoSQL database with structured, unstructured and semi-structured data. As the continuous development of applications is taking place, a huge volume of data collected has already been taken for architectural migration from SQL database to NoSQL database. Since NoSQL is emerging and evolving technology in the field of database management and because of increased maturity of NoSQL database technology, many applications have already switched to NoSQL so that extracting information from big data. This study discusses, analyzes and compares 7 (seven) different techniques of data migration from SQL database to NoSQL database. The migration is performed by using appropriated tools / frameworks available for each technique and the results are evaluated, analyzed and validated using a system tool called SysGauge. The parameters used for the analysis and the comparison are Speed, Execution Time, Maximum CPU Usage and Maximum Memory Usage. At the end of the entire work, the most efficient techniques have been recommended.","publication_date":{"day":28,"month":12,"year":2020,"errors":{}},"publication_name":"Journal of computer engineering \u0026 information technology","grobid_abstract_attachment_id":107603551},"translated_abstract":null,"internal_url":"https://www.academia.edu/109494622/Comparison_of_Data_MigrationTechniques_from_SQL_Databaseto_NoSQL_Database","translated_internal_url":"","created_at":"2023-11-20T19:27:11.988-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":107603551,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107603551/thumbnails/1.jpg","file_name":"download.pdf","download_url":"https://www.academia.edu/attachments/107603551/download_file","bulk_download_file_name":"Comparison_of_Data_MigrationTechniques_f.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107603551/download-libre.pdf?1700539789=\u0026response-content-disposition=attachment%3B+filename%3DComparison_of_Data_MigrationTechniques_f.pdf\u0026Expires=1743492636\u0026Signature=UKhscsZG99k73Q1rXvekUp61lbgxvB8F~dKvErl59VrHPjTqssvyM~kyby86OhB1-Ix6fZq-tfKz~Js1gP8eMr7d37G2zCVR8Jw9dGuU~Q-bjVt3LcIOkXxS4csDi2nD4kqqLBh82ius7~jbLJgG6cBbX7ccLzhs~KMtIoKEeiLK6HVHMtiJrorcN9lZJeYAroY6UmMi~qcmS6GfWtVYkppxbrcKuY-mMnJik3Vk7oOVYcKlwnt9kGFMh-GyQd~-ddDXU69LY5zeDnUX1hHItc3EBms7uhtFgN4v3seOSL~gvVA5Srh-dgL21bE6j09MV3KXQ2vvaygIr0T4niCteQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Comparison_of_Data_MigrationTechniques_from_SQL_Databaseto_NoSQL_Database","translated_slug":"","page_count":10,"language":"en","content_type":"Work","summary":"With rapid and multi-dimensional growth of data, Relational Database Management System (RDBMS) having Structured Query Language (SQL) support is facing difficulties in managing huge data due to lack of dynamic data model, performance and scalability issues etc. NoSQL database addresses these issues by providing the features that SQL database lacks. So, many organizations are migrating from SQL to NoSQL. RDBMS database deals with structured data and NoSQL database with structured, unstructured and semi-structured data. As the continuous development of applications is taking place, a huge volume of data collected has already been taken for architectural migration from SQL database to NoSQL database. Since NoSQL is emerging and evolving technology in the field of database management and because of increased maturity of NoSQL database technology, many applications have already switched to NoSQL so that extracting information from big data. This study discusses, analyzes and compares 7 (seven) different techniques of data migration from SQL database to NoSQL database. The migration is performed by using appropriated tools / frameworks available for each technique and the results are evaluated, analyzed and validated using a system tool called SysGauge. The parameters used for the analysis and the comparison are Speed, Execution Time, Maximum CPU Usage and Maximum Memory Usage. 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In this contex...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection&quot; algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494621"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494621"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494621; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109494621]").text(description); $(".js-view-count[data-work-id=109494621]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109494621; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109494621']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=109494621]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109494621,"title":"Hybrid Intrusion Detection","translated_title":"","metadata":{"abstract":"Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection\u0026quot; algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.","publisher":"LAP LAMBERT Academic Publishing","publication_date":{"day":14,"month":11,"year":2016,"errors":{}},"publication_name":"LAP LAMBERT Academic Publishing eBooks"},"translated_abstract":"Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection\u0026quot; algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.","internal_url":"https://www.academia.edu/109494621/Hybrid_Intrusion_Detection","translated_internal_url":"","created_at":"2023-11-20T19:27:11.566-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Hybrid_Intrusion_Detection","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection\u0026quot; algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.","owner":{"id":371695,"first_name":"Roshan","middle_initials":null,"last_name":"Chitrakar","page_name":"RoshanChitrakar","domain_name":"ncit","created_at":"2011-03-21T15:54:06.502-07:00","display_name":"Roshan Chitrakar","url":"https://ncit.academia.edu/RoshanChitrakar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining"},{"id":31349,"name":"Anomaly Detection","url":"https://www.academia.edu/Documents/in/Anomaly_Detection"},{"id":131237,"name":"Cluster Analysis","url":"https://www.academia.edu/Documents/in/Cluster_Analysis"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine"},{"id":320736,"name":"Intrusion Detection System","url":"https://www.academia.edu/Documents/in/Intrusion_Detection_System"}],"urls":[{"id":35622771,"url":"https://www.knigozal.com/store/gb/book/hybrid-intrusion-detection/isbn/978-3-659-97921-7"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-109494621-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="109494620"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/109494620/Selection_of_Candidate_Support_Vectors_in_incremental_SVM_for_network_intrusion_detection"><img alt="Research paper thumbnail of Selection of Candidate Support Vectors in incremental SVM for network intrusion detection" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Selection of Candidate Support Vectors in incremental SVM for network intrusion detection</div><div class="wp-workCard_item"><span>Computers & Security</span><span>, Sep 1, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as no...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494620"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494620"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494620; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109494620]").text(description); $(".js-view-count[data-work-id=109494620]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109494620; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109494620']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=109494620]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109494620,"title":"Selection of Candidate Support Vectors in incremental SVM for network intrusion detection","translated_title":"","metadata":{"abstract":"ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.","publisher":"Elsevier BV","publication_date":{"day":1,"month":9,"year":2014,"errors":{}},"publication_name":"Computers \u0026 Security"},"translated_abstract":"ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.","internal_url":"https://www.academia.edu/109494620/Selection_of_Candidate_Support_Vectors_in_incremental_SVM_for_network_intrusion_detection","translated_internal_url":"","created_at":"2023-11-20T19:27:11.157-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Selection_of_Candidate_Support_Vectors_in_incremental_SVM_for_network_intrusion_detection","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.","owner":{"id":371695,"first_name":"Roshan","middle_initials":null,"last_name":"Chitrakar","page_name":"RoshanChitrakar","domain_name":"ncit","created_at":"2011-03-21T15:54:06.502-07:00","display_name":"Roshan Chitrakar","url":"https://ncit.academia.edu/RoshanChitrakar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3703,"name":"Network Security","url":"https://www.academia.edu/Documents/in/Network_Security"},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine"},{"id":320736,"name":"Intrusion Detection System","url":"https://www.academia.edu/Documents/in/Intrusion_Detection_System"},{"id":1164759,"name":"Computers Security","url":"https://www.academia.edu/Documents/in/Computers_Security"}],"urls":[{"id":35622770,"url":"https://doi.org/10.1016/j.cose.2014.06.006"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-109494620-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="109494619"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109494619/Hybrid_Approaches_to_Block_Cipher"><img alt="Research paper thumbnail of Hybrid Approaches to Block Cipher" class="work-thumbnail" src="https://attachments.academia-assets.com/107603495/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109494619/Hybrid_Approaches_to_Block_Cipher">Hybrid Approaches to Block Cipher</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This chapter introduces two new approaches to block cipher-one is DNA hybridization encryption sc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This chapter introduces two new approaches to block cipher-one is DNA hybridization encryption scheme (DHES) and the other is hybrid graphical encryption algorithm (HGEA). DNA cryptography deals with the techniques of hiding messages in the form of a DNA sequence. The key size of data encryption standard (DES) can be increased by using DHES. In DHES, DNA cryptography algorithm is used for encryption and decryption, and one-time pad (OTP) scheme is used for key generation. The output of DES algorithm is passed as an input to DNA hybridization scheme to provide an added security. The second approach, HGEA, is based on graphical pattern recognition. By performing multiple transformations, shifting and logical operations, a block cipher is obtained. This algorithm is influenced by hybrid cubes encryption algorithm (HiSea). Features like graphical interpretation and computation of selected quadrant value are the unique features of HGEA. Moreover, multiple key generation scheme combined with graphical interpretation method provides an increased level of security.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-109494619-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-109494619-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353814/figure-1-hybrid-approaches-to-block-cipher"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/107603495/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353817/figure-2-ees-flow-chart-of-dna-hybridization-decryption"><img alt="Figure 2. a © ees Flow chart of DNA hybridization decryption. " class="figure-slide-image" src="https://figures.academia-assets.com/107603495/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353835/figure-3-key-this-is-because-the-length-of-ciphertext"><img alt="key; this is because the length of ciphertext depends upon the number of 1s present in the input plaintext. " class="figure-slide-image" src="https://figures.academia-assets.com/107603495/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353845/figure-4-this-encryption-algorithm-can-process-any-plaintext"><img alt="This encryption algorithm can process any “n” plaintext ASCII characters from input file. The input string is split into 8 bytes of m parts. Then, the input ASCII message bit is put up against the standard ASCII table. The plaintext value is then replaced by its ASCII value according to the table. This encryption encompasses numbers, special characters and even spaces. " class="figure-slide-image" src="https://figures.academia-assets.com/107603495/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353852/figure-5-hybrid-approaches-to-block-cipher"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/107603495/figure_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353861/figure-6-hybrid-approaches-to-block-cipher"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/107603495/figure_006.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353872/figure-5-file-size-vs-execution-time-of-des-and-hgea-the"><img alt="Figure 5. File size vs. execution time of DES and HGEA. The DDHO algorithm is tested on different types of plaintext; the encryption and decryption times are calculated; the analysis of length of plaintext, length of ciphertext and size of key is done and found that the length of ciphertext is pro- portional to the corresponding plaintext length. The encryption and decryption times increase slower with the changes in the length of plaintext. Y Y a From the process of analyzing various cryptographic algorithms, a unique encryption algorithm “hybrid graphical encryption algorithm” has been proposed. The algorithm was based on hybrid cubes encryption algorithm (HiSea). The fea- tures like graphical interpretation and computation of selected quadrant value are the unique features of this algorithm, which is different from existing standard " class="figure-slide-image" src="https://figures.academia-assets.com/107603495/figure_007.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353887/table-2-performance-of-ddho-with-plaintexts-of-different"><img alt="Table 2. Performance of DDHO with plaintexts of different lengths and contents Plaintext of different contents for DDHO algorithm. " class="figure-slide-image" src="https://figures.academia-assets.com/107603495/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353891/table-2-hybrid-approaches-to-block-cipher"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/107603495/table_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353908/table-3-hybrid-approaches-to-block-cipher"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/107603495/table_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353919/table-4-hybrid-approaches-to-block-cipher"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/107603495/table_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353939/table-5-ll-the-average-values-of-execution-time-for"><img alt="ll The average values of execution time for encryption and decryption were com- puted and are tabulated in Tables 12 and 13. " class="figure-slide-image" src="https://figures.academia-assets.com/107603495/table_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353956/table-6-test-results-hgea-execution-time-of-hges"><img alt="Table: Test results HGEA Execution time of HGES. 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To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494618"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494618"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494618; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109494618]").text(description); $(".js-view-count[data-work-id=109494618]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109494618; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109494618']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=109494618]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109494618,"title":"Anomaly detection using Support Vector Machine classification with k-Medoids clustering","translated_title":"","metadata":{"abstract":"ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.","publication_date":{"day":1,"month":11,"year":2012,"errors":{}}},"translated_abstract":"ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.","internal_url":"https://www.academia.edu/109494618/Anomaly_detection_using_Support_Vector_Machine_classification_with_k_Medoids_clustering","translated_internal_url":"","created_at":"2023-11-20T19:27:10.751-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Anomaly_detection_using_Support_Vector_Machine_classification_with_k_Medoids_clustering","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.","owner":{"id":371695,"first_name":"Roshan","middle_initials":null,"last_name":"Chitrakar","page_name":"RoshanChitrakar","domain_name":"ncit","created_at":"2011-03-21T15:54:06.502-07:00","display_name":"Roshan Chitrakar","url":"https://ncit.academia.edu/RoshanChitrakar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining"},{"id":3703,"name":"Network Security","url":"https://www.academia.edu/Documents/in/Network_Security"},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines"},{"id":31349,"name":"Anomaly Detection","url":"https://www.academia.edu/Documents/in/Anomaly_Detection"},{"id":131237,"name":"Cluster Analysis","url":"https://www.academia.edu/Documents/in/Cluster_Analysis"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine"},{"id":315668,"name":"Svm","url":"https://www.academia.edu/Documents/in/Svm"},{"id":320736,"name":"Intrusion Detection System","url":"https://www.academia.edu/Documents/in/Intrusion_Detection_System"},{"id":795003,"name":"Linear Regression","url":"https://www.academia.edu/Documents/in/Linear_Regression"},{"id":999285,"name":"Handwritten Digit Classification","url":"https://www.academia.edu/Documents/in/Handwritten_Digit_Classification"},{"id":999290,"name":"Multi Class Classification","url":"https://www.academia.edu/Documents/in/Multi_Class_Classification"},{"id":1032327,"name":"False Positive Rate","url":"https://www.academia.edu/Documents/in/False_Positive_Rate"},{"id":1553450,"name":"Naive Bayes Classifier","url":"https://www.academia.edu/Documents/in/Naive_Bayes_Classifier"}],"urls":[{"id":35622768,"url":"https://doi.org/10.1109/ahici.2012.6408446"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-109494618-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="109494617"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/109494617/Anomaly_Based_Intrusion_Detection_Using_Hybrid_Learning_Approach_of_Combining_k_Medoids_Clustering_and_Na%C3%AFve_Bayes_Classification"><img alt="Research paper thumbnail of Anomaly Based Intrusion Detection Using Hybrid Learning Approach of Combining k-Medoids Clustering and Naïve Bayes Classification" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Anomaly Based Intrusion Detection Using Hybrid Learning Approach of Combining k-Medoids Clustering and Naïve Bayes Classification</div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Informat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494617"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494617"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494617; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109494617]").text(description); $(".js-view-count[data-work-id=109494617]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109494617; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109494617']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=109494617]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109494617,"title":"Anomaly Based Intrusion Detection Using Hybrid Learning Approach of Combining k-Medoids Clustering and Naïve Bayes Classification","translated_title":"","metadata":{"abstract":"ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. 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An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.","internal_url":"https://www.academia.edu/109494617/Anomaly_Based_Intrusion_Detection_Using_Hybrid_Learning_Approach_of_Combining_k_Medoids_Clustering_and_Na%C3%AFve_Bayes_Classification","translated_internal_url":"","created_at":"2023-11-20T19:27:10.565-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Anomaly_Based_Intrusion_Detection_Using_Hybrid_Learning_Approach_of_Combining_k_Medoids_Clustering_and_Naïve_Bayes_Classification","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-109494617-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="109494616"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/109494616/Intrusion_Detection_Based_on_PCA_with_Improved_K_Means"><img alt="Research paper thumbnail of Intrusion Detection Based on PCA with Improved K-Means" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Intrusion Detection Based on PCA with Improved K-Means</div><div class="wp-workCard_item"><span>Springer eBooks</span><span>, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494616"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494616"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494616; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-109494616-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="109494614"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109494614/Integration_of_Expectation_Maximization_using_Gaussian_Mixture_Models_and_Na%C3%AFve_Bayes_for_Intrusion_Detection"><img alt="Research paper thumbnail of Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection" class="work-thumbnail" src="https://attachments.academia-assets.com/107603550/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109494614/Integration_of_Expectation_Maximization_using_Gaussian_Mixture_Models_and_Na%C3%AFve_Bayes_for_Intrusion_Detection">Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection</a></div><div class="wp-workCard_item"><span>Journal of computer science research</span><span>, Apr 20, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Intrusion detection is the investigation process of information about the system activities or it...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity. Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability. Nonetheless, it is Naïve use of the mean data value for the cluster core that presents a major drawback. The chances of two circular clusters having different radius and centering at the same mean will occur. This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together. However, if the clusters are not spherical, it fails. To overcome this issue, a new integrated hybrid model by integrating expectation maximizing (EM) clustering using a Gaussian mixture model (GMM) and naïve Bays classifier have been proposed. In this model, GMM give more flexibility than K-Means in terms of cluster covariance. Also, they use probabilities function and soft clustering, that's why they can have multiple cluster for a single data. In GMM, we can define the cluster form in GMM by two parameters: the mean and the standard deviation. This means that by using these two parameters, the cluster can take any kind of elliptical shape. EM-GMM will be used to cluster data based on data activity into the corresponding category.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="78a59d1a91329c816739240b8c066b40" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107603550,"asset_id":109494614,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107603550/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494614"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494614"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494614; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109494614]").text(description); $(".js-view-count[data-work-id=109494614]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109494614; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109494614']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "78a59d1a91329c816739240b8c066b40" } } $('.js-work-strip[data-work-id=109494614]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109494614,"title":"Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection","translated_title":"","metadata":{"publisher":"Bilingual Publishing Co.","ai_title_tag":"EM-GMM and Naïve Bayes for Intrusion Detection","grobid_abstract":"Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity. Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability. Nonetheless, it is Naïve use of the mean data value for the cluster core that presents a major drawback. The chances of two circular clusters having different radius and centering at the same mean will occur. This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together. However, if the clusters are not spherical, it fails. To overcome this issue, a new integrated hybrid model by integrating expectation maximizing (EM) clustering using a Gaussian mixture model (GMM) and naïve Bays classifier have been proposed. In this model, GMM give more flexibility than K-Means in terms of cluster covariance. Also, they use probabilities function and soft clustering, that's why they can have multiple cluster for a single data. In GMM, we can define the cluster form in GMM by two parameters: the mean and the standard deviation. This means that by using these two parameters, the cluster can take any kind of elliptical shape. EM-GMM will be used to cluster data based on data activity into the corresponding category.","publication_date":{"day":20,"month":4,"year":2021,"errors":{}},"publication_name":"Journal of computer science research","grobid_abstract_attachment_id":107603550},"translated_abstract":null,"internal_url":"https://www.academia.edu/109494614/Integration_of_Expectation_Maximization_using_Gaussian_Mixture_Models_and_Na%C3%AFve_Bayes_for_Intrusion_Detection","translated_internal_url":"","created_at":"2023-11-20T19:27:09.951-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":107603550,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107603550/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/107603550/download_file","bulk_download_file_name":"Integration_of_Expectation_Maximization.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107603550/pdf-libre.pdf?1700539790=\u0026response-content-disposition=attachment%3B+filename%3DIntegration_of_Expectation_Maximization.pdf\u0026Expires=1743492637\u0026Signature=IowNDcJWz-8Jb0XOsV5ybM8Bgx0hQOLSuBl9afMCUu3fL5DWJGaTNAclhYoma75T6XeeyGJguGRi-3w-icA9nVLF4XOdub5I343tsKzyHsNtV9xcIoEjTjATPx5zGs4TvPuR6iS-C3xceb~PnxHvAnC3rPBaqDAIqLNJ0j4gxbNvt64yXv~pFvaAIks9ZKBSoWf866GoQ0BSZxPIs8o5WZghq3OuKWr6ooNJ0yPtlqr~Nu6Ml~5HuxPuYrxkJTvy9zVEy6mE0iakLnXca~lXinOXILfNKiVKdMq~7jSlMNo~8INoMnVf5ePVD6I71vW6elBmQCUxXDENOy23LdJpdw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Integration_of_Expectation_Maximization_using_Gaussian_Mixture_Models_and_Naïve_Bayes_for_Intrusion_Detection","translated_slug":"","page_count":10,"language":"en","content_type":"Work","summary":"Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity. Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability. Nonetheless, it is Naïve use of the mean data value for the cluster core that presents a major drawback. The chances of two circular clusters having different radius and centering at the same mean will occur. This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together. However, if the clusters are not spherical, it fails. To overcome this issue, a new integrated hybrid model by integrating expectation maximizing (EM) clustering using a Gaussian mixture model (GMM) and naïve Bays classifier have been proposed. In this model, GMM give more flexibility than K-Means in terms of cluster covariance. Also, they use probabilities function and soft clustering, that's why they can have multiple cluster for a single data. In GMM, we can define the cluster form in GMM by two parameters: the mean and the standard deviation. This means that by using these two parameters, the cluster can take any kind of elliptical shape. 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The main objective of this paper is to propose a system that effectively detects DDoS attacks appearing in any networked system using the clustering technique of data mining followed by classification. This method uses a Heuristics Clustering Algorithm (HCA) to cluster the available data and Naïve Bayes (NB) classification to classify the data and detect the attacks created in the system based on some network attributes of the data packet. The clustering algorithm is based in unsupervised learning technique and is sometimes unable to detect some of the attack instances and few normal instances, therefore classification techniques are also used along with clustering to overcome this classification problem and to enhance the accuracy. Naïve Bayes classifiers are based on very strong independence assumptions with fairly simple construction to derive the conditional probability for each relationship. A series of experiment is performed using "The CAIDA UCSD DDoS Attack 2007 Dataset" and "DARPA 2000 Dataset" and the efficiency of the proposed system has been tested based on the following performance parameters: Accuracy, Detection Rate and False Positive Rate and the result obtained from the proposed system has been found that it has enhanced accuracy and detection rate with low false positive rate.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="60424b8af0b56da40bd13ebb106b4299" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107603501,"asset_id":109494479,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107603501/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494479"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494479"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494479; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109494479]").text(description); $(".js-view-count[data-work-id=109494479]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109494479; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109494479']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "60424b8af0b56da40bd13ebb106b4299" } } $('.js-work-strip[data-work-id=109494479]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109494479,"title":"DDoS Attack Detection Using Heuristics Clustering Algorithm and Na\u0026#239;ve Bayes Classification","translated_title":"","metadata":{"publisher":"Scientific Research Publishing","ai_title_tag":"DDoS Detection via Heuristic Clustering and Naïve Bayes","grobid_abstract":"In recent times among the multitude of attacks present in network system, DDoS attacks have emerged to be the attacks with the most devastating effects. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-91184838-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="79505191"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79505191/Physical_Health_Problems_and_Patterns_of_Self_Care_Associated_with_the_Use_of_Digital_Devices_among_University_Students"><img alt="Research paper thumbnail of Physical Health Problems and Patterns of Self-Care Associated with the Use of Digital Devices among University Students" class="work-thumbnail" src="https://attachments.academia-assets.com/86197456/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79505191/Physical_Health_Problems_and_Patterns_of_Self_Care_Associated_with_the_Use_of_Digital_Devices_among_University_Students">Physical Health Problems and Patterns of Self-Care Associated with the Use of Digital Devices among University Students</a></div><div class="wp-workCard_item"><span>MedS Alliance Journal of Medicine and Medical Sciences</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">INTRODUCTION: Utilization of digital devices create some problems for users, such as, mental diso...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">INTRODUCTION: Utilization of digital devices create some problems for users, such as, mental disorder, visual problems, headache, weight gain and unnecessary time consumption. Therefore, this study aims to identify the information about the practices on the use of digital devices, its impact on physical health and pattern of self-care among the university students involved in different professions. MATERIALS AND METHODS: MPhil scholars involved in different professions (n= 315) of Nepal Open University (NOU) had participated in this cross-sectional online survey during January 2019 to August 2019. Multivariable analysis was employed to obtain rate ratios and chi-square test was used for the association of the use of digital devices with physical health problems. RESULTS: Socio-demographic factors like age was significantly associated with neck pain (p=0.02) and stiffness in hands/arms (p=0.04), while profession was associated with weight gain and difficulty in sleep (p=0.04). Moreov...</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-79505191-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-79505191-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/46328203/figure-1-took-rest-when-they-suffered-from-headache-neck"><img alt="took rest when they suffered from headache (54.2 %), neck pain (53.6 %), back pain (55.7 %), strain on hands and arms (59.4 %) and eye strain (49.1 %). Moreover, a higher proportion of participants had no treatment for sleep difficulty, passiveness of body and weight gain. Around one-fourth of the participants either took medicine or did meditation to get rid of physical health problems. Figure 1| Visualization of headache, eye problem, back pain, and neck pain and their treatment measures body and weight gain. Around one-fourth of the Patterns of treatment having a physical health problem among university students " class="figure-slide-image" src="https://figures.academia-assets.com/86197456/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/46328208/figure-2-visualization-of-train-hands-arms-sleep-difficulty"><img alt="Figure 2! Visualization of train hands/arms, sleep difficulty, passiveness of body and weight gain, and their " class="figure-slide-image" src="https://figures.academia-assets.com/86197456/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/46328211/table-1-socio-demographic-characteristics-and-the-use-of"><img alt="Table 1! Socio-demographic characteristics and the use of higher among respondents aged 41-60 years, whereas headache, back pain, strain on hands and arms, sleep difficulties, and body’s passiveness were higher among respondents aged 20-40 years. A significant association was found across socio- demographic characteristics and physical health problems like age group with neck pain (x2 = 5.081, p= 0.02, Phi=-0.13) and strain in hands/arms (x2 = 4.46, p= 0.04, Phi=0.13), profession with weight gain x2 = 4.2, p= 0.04, Phi=-0.12). In context to the profession, all types of physical health problems were higher among participants engaged in teaching profession however, weight gain was higher among participants who were involved in non-teaching profession (x2 = 4.19, p= 0.04, Phi=0.13). Likewise, sleep difficulty was greater among participants with work experience of less than ten years (x2 = 4.19, p= 0.04, Phi=0.13). However, weight gain was higher among participants with work experience of more than ten years (x2 = 4.57, p=0.03, Phi=-0.13). Moreover, the proportion of respondents with headache was higher among those who took tablets " class="figure-slide-image" src="https://figures.academia-assets.com/86197456/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/46328223/table-2-association-of-physical-health-problems-with-socio"><img alt="Table 2! Association of physical health problems with socio-demographic characteristics (n=315) " class="figure-slide-image" src="https://figures.academia-assets.com/86197456/table_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/46328236/table-3-physical-health-problems-and-patterns-of-self-care"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/86197456/table_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/46328251/table-4-between-the-remaining-physical-health-problems-with"><img alt="between the remaining physical health problems with the daily use of digital devices. " class="figure-slide-image" src="https://figures.academia-assets.com/86197456/table_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/46328262/table-5-pattern-of-treatment-having-physical-health-problems"><img alt="Table 5| Pattern of treatment having physical health problems among university students (n=315) " class="figure-slide-image" src="https://figures.academia-assets.com/86197456/table_005.jpg" /></a></figure></div><div class="next-slide-container js-next-button-container"><button aria-label="Next" class="carousel-navigation-button js-profile-work-79505191-figures-next"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_forward_ios</span></button></div></div></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="03abb542b3955d6647aff817b071727a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86197456,"asset_id":79505191,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86197456/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79505191"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79505191"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79505191; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79505191]").text(description); $(".js-view-count[data-work-id=79505191]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79505191; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79505191']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "03abb542b3955d6647aff817b071727a" } } $('.js-work-strip[data-work-id=79505191]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79505191,"title":"Physical Health Problems and Patterns of Self-Care Associated with the Use of Digital Devices among University Students","translated_title":"","metadata":{"abstract":"INTRODUCTION: Utilization of digital devices create some problems for users, such as, mental disorder, visual problems, headache, weight gain and unnecessary time consumption. 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Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79505190"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79505190"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79505190; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79505190]").text(description); $(".js-view-count[data-work-id=79505190]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79505190; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79505190']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=79505190]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79505190,"title":"Anomaly Based Intrusion Detection Using Hybrid Learning Approach of Combining k-Medoids Clustering and Na\u0026#239;ve Bayes Classification","translated_title":"","metadata":{"abstract":"ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2012,"errors":{}},"publication_name":"2012 8th International Conference on Wireless Communications, Networking and Mobile Computing"},"translated_abstract":"ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.","internal_url":"https://www.academia.edu/79505190/Anomaly_Based_Intrusion_Detection_Using_Hybrid_Learning_Approach_of_Combining_k_Medoids_Clustering_and_Na_and_239_ve_Bayes_Classification","translated_internal_url":"","created_at":"2022-05-20T03:01:50.091-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Anomaly_Based_Intrusion_Detection_Using_Hybrid_Learning_Approach_of_Combining_k_Medoids_Clustering_and_Na_and_239_ve_Bayes_Classification","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.","owner":{"id":371695,"first_name":"Roshan","middle_initials":null,"last_name":"Chitrakar","page_name":"RoshanChitrakar","domain_name":"ncit","created_at":"2011-03-21T15:54:06.502-07:00","display_name":"Roshan Chitrakar","url":"https://ncit.academia.edu/RoshanChitrakar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":1283,"name":"Information Security","url":"https://www.academia.edu/Documents/in/Information_Security"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining"},{"id":3703,"name":"Network Security","url":"https://www.academia.edu/Documents/in/Network_Security"},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines"},{"id":131237,"name":"Cluster Analysis","url":"https://www.academia.edu/Documents/in/Cluster_Analysis"},{"id":315668,"name":"Svm","url":"https://www.academia.edu/Documents/in/Svm"},{"id":320736,"name":"Intrusion Detection System","url":"https://www.academia.edu/Documents/in/Intrusion_Detection_System"},{"id":795003,"name":"Linear Regression","url":"https://www.academia.edu/Documents/in/Linear_Regression"},{"id":999285,"name":"Handwritten Digit Classification","url":"https://www.academia.edu/Documents/in/Handwritten_Digit_Classification"},{"id":999290,"name":"Multi Class Classification","url":"https://www.academia.edu/Documents/in/Multi_Class_Classification"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-79505190-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="79505189"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/79505189/Anomaly_detection_using_Support_Vector_Machine_classification_with_k_Medoids_clustering"><img alt="Research paper thumbnail of Anomaly detection using Support Vector Machine classification with k-Medoids clustering" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Anomaly detection using Support Vector Machine classification with k-Medoids clustering</div><div class="wp-workCard_item"><span>2012 Third Asian Himalayas International Conference on Internet</span><span>, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79505189"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79505189"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79505189; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79505189]").text(description); $(".js-view-count[data-work-id=79505189]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79505189; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79505189']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=79505189]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79505189,"title":"Anomaly detection using Support Vector Machine classification with k-Medoids clustering","translated_title":"","metadata":{"abstract":"ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2012,"errors":{}},"publication_name":"2012 Third Asian Himalayas International Conference on Internet"},"translated_abstract":"ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.","internal_url":"https://www.academia.edu/79505189/Anomaly_detection_using_Support_Vector_Machine_classification_with_k_Medoids_clustering","translated_internal_url":"","created_at":"2022-05-20T03:01:49.868-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":38271545,"work_id":79505189,"tagging_user_id":371695,"tagged_user_id":null,"co_author_invite_id":694255,"email":"h***h@whu.edu.cn","display_order":0,"name":"Chuanhe Huang","title":"Anomaly detection using Support Vector Machine classification with k-Medoids clustering"}],"downloadable_attachments":[],"slug":"Anomaly_detection_using_Support_Vector_Machine_classification_with_k_Medoids_clustering","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.","owner":{"id":371695,"first_name":"Roshan","middle_initials":null,"last_name":"Chitrakar","page_name":"RoshanChitrakar","domain_name":"ncit","created_at":"2011-03-21T15:54:06.502-07:00","display_name":"Roshan Chitrakar","url":"https://ncit.academia.edu/RoshanChitrakar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":3703,"name":"Network Security","url":"https://www.academia.edu/Documents/in/Network_Security"},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines"},{"id":315668,"name":"Svm","url":"https://www.academia.edu/Documents/in/Svm"},{"id":795003,"name":"Linear Regression","url":"https://www.academia.edu/Documents/in/Linear_Regression"},{"id":999285,"name":"Handwritten Digit Classification","url":"https://www.academia.edu/Documents/in/Handwritten_Digit_Classification"},{"id":999290,"name":"Multi Class Classification","url":"https://www.academia.edu/Documents/in/Multi_Class_Classification"}],"urls":[{"id":20610048,"url":"http://xplorestaging.ieee.org/ielx5/6396223/6408322/06408446.pdf?arnumber=6408446"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-79505189-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="79505188"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/79505188/Selection_of_Candidate_Support_Vectors_in_incremental_SVM_for_network_intrusion_detection"><img alt="Research paper thumbnail of Selection of Candidate Support Vectors in incremental SVM for network intrusion detection" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Selection of Candidate Support Vectors in incremental SVM for network intrusion detection</div><div class="wp-workCard_item"><span>Computers &amp; Security</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as no...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79505188"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79505188"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79505188; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79505188]").text(description); $(".js-view-count[data-work-id=79505188]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79505188; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79505188']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=79505188]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79505188,"title":"Selection of Candidate Support Vectors in incremental SVM for network intrusion detection","translated_title":"","metadata":{"abstract":"ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Computers \u0026amp; Security"},"translated_abstract":"ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.","internal_url":"https://www.academia.edu/79505188/Selection_of_Candidate_Support_Vectors_in_incremental_SVM_for_network_intrusion_detection","translated_internal_url":"","created_at":"2022-05-20T03:01:49.499-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Selection_of_Candidate_Support_Vectors_in_incremental_SVM_for_network_intrusion_detection","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.","owner":{"id":371695,"first_name":"Roshan","middle_initials":null,"last_name":"Chitrakar","page_name":"RoshanChitrakar","domain_name":"ncit","created_at":"2011-03-21T15:54:06.502-07:00","display_name":"Roshan Chitrakar","url":"https://ncit.academia.edu/RoshanChitrakar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines"},{"id":320736,"name":"Intrusion Detection System","url":"https://www.academia.edu/Documents/in/Intrusion_Detection_System"},{"id":1164759,"name":"Computers Security","url":"https://www.academia.edu/Documents/in/Computers_Security"}],"urls":[{"id":20610047,"url":"https://api.elsevier.com/content/article/PII:S0167404814000996?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-79505188-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="79505153"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79505153/DDoS_Attack_Detection_Using_Heuristics_Clustering_Algorithm_and_Na%C3%AFve_Bayes_Classification"><img alt="Research paper thumbnail of DDoS Attack Detection Using Heuristics Clustering Algorithm and Naïve Bayes Classification" class="work-thumbnail" src="https://attachments.academia-assets.com/86197460/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79505153/DDoS_Attack_Detection_Using_Heuristics_Clustering_Algorithm_and_Na%C3%AFve_Bayes_Classification">DDoS Attack Detection Using Heuristics Clustering Algorithm and Naïve Bayes Classification</a></div><div class="wp-workCard_item"><span>Journal of Information Security</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In recent times among the multitude of attacks present in network system, DDoS attacks have emerg...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In recent times among the multitude of attacks present in network system, DDoS attacks have emerged to be the attacks with the most devastating effects. The main objective of this paper is to propose a system that effectively detects DDoS attacks appearing in any networked system using the clustering technique of data mining followed by classification. This method uses a Heuristics Clustering Algorithm (HCA) to cluster the available data and Naïve Bayes (NB) classification to classify the data and detect the attacks created in the system based on some network attributes of the data packet. The clustering algorithm is based in unsupervised learning technique and is sometimes unable to detect some of the attack instances and few normal instances, therefore classification techniques are also used along with clustering to overcome this classification problem and to enhance the accuracy. Naïve Bayes classifiers are based on very strong independence assumptions with fairly simple construction to derive the conditional probability for each relationship. A series of experiment is performed using "The CAIDA UCSD DDoS Attack 2007 Dataset" and "DARPA 2000 Dataset" and the efficiency of the proposed system has been tested based on the following performance parameters: Accuracy, Detection Rate and False Positive Rate and the result obtained from the proposed system has been found that it has enhanced accuracy and detection rate with low false positive rate.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-79505153-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-79505153-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553871/figure-1-system-block-diagram-processing-those-datasets-are"><img alt="Figure 1. System block diagram. processing, those datasets are fed into Heuristics Clustering Algorithm that results would ultimately result in wrong output. Once, the dataset is prepared after pre- tasets followed by the preprocessing of data to eliminate those data values that " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553884/figure-2-improvement-in-accuracy-with-hca-clustering"><img alt="Improvement in Accuracy with HCA Clustering followed by NB Classification Figure 2. Improvement in Accuracy with HCA Followed by NB Classification. " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553891/figure-3-improvement-in-detection-rate-with-hca-clustering"><img alt="Figure 3. Improvement in Detection Rate with HCA Clustering followed by NB Classif cation. " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553896/figure-4-improvement-in-false-positive-rate-with-hca"><img alt="Figure 4. Improvement in false positive rate with HCA clustering followed by NB classi- fication. " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553901/table-1-comparison-of-accuracy-in-caida-ucsd-ddos-attack"><img alt="Table 1. Comparison of accuracy in CAIDA UCSD DDoS attack 2007 dataset. " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553908/table-2-comparison-of-accuracy-in-darpa-dataset-comparison"><img alt="Table 2. Comparison of accuracy in DARPA 2000 dataset. Table 3. Comparison of Detection Rate in CAIDA UCSD DDoS Attack 2007 Dataset. " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/table_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553911/table-4-comparison-of-detection-rate-in-darpa-dataset"><img alt="Table 4. Comparison of detection rate in DARPA 2000 dataset. " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/table_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553917/table-4-ddos-attack-detection-using-heuristics-clustering"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/86197460/table_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553928/table-5-comparison-of-false-positive-rate-in-caida-ucsd-ddos"><img alt="Table 5. Comparison of false positive rate in CAIDA UCSD DDoS attack 2007. Table 6. Comparison of false positive rate in DARPA 2000 dataset. " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/table_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553936/table-6-ddos-attack-detection-using-heuristics-clustering"><img alt="Performance Analysis " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/table_006.jpg" /></a></figure></div><div class="next-slide-container js-next-button-container"><button aria-label="Next" class="carousel-navigation-button js-profile-work-79505153-figures-next"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_forward_ios</span></button></div></div></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="76e8a9429a90beabb2fc6a11597865f1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86197460,"asset_id":79505153,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86197460/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79505153"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79505153"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79505153; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79505153]").text(description); $(".js-view-count[data-work-id=79505153]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79505153; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79505153']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "76e8a9429a90beabb2fc6a11597865f1" } } $('.js-work-strip[data-work-id=79505153]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79505153,"title":"DDoS Attack Detection Using Heuristics Clustering Algorithm and Naïve Bayes Classification","translated_title":"","metadata":{"publisher":"Scientific Research Publishing, Inc,","grobid_abstract":"In recent times among the multitude of attacks present in network system, DDoS attacks have emerged to be the attacks with the most devastating effects. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-125390914-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="125390913"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/125390913/Measurement_of_Kernel_Preemption_Time_of_Real_Time_Operating_Systems"><img alt="Research paper thumbnail of Measurement of Kernel Preemption Time of Real Time Operating Systems" class="work-thumbnail" src="https://attachments.academia-assets.com/119443361/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/125390913/Measurement_of_Kernel_Preemption_Time_of_Real_Time_Operating_Systems">Measurement of Kernel Preemption Time of Real Time Operating Systems</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In a Real Time System, when a high priority task is scheduled, it preempts ongoing lower priority...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In a Real Time System, when a high priority task is scheduled, it preempts ongoing lower priority tasks and sometimes it preempts even the kernel too. In normal cases, the time needed for the preemption is too small and hence is not considered in task scheduling. But, in some mission critical hard real time systems, even a small fraction of a second should be taken into account as it could affect the deadline. This research work proposes two models -embedded model and software model, among which the latter has been used for the experiment that calculates the kernel preemption time. The hardware model consists of a triangular wave generator called TRIANG that takes timer values from real time tasks and sends outputs to a comparator module. Finally, the comparator compares the time differences, and hence, calculates the kernel preemption time. Whereas the software model uses the RT-Linux architecture in which device drivers are used instead of hardware components. A high precision timer clock is also used to measure the task release times. The results from the experiments show that the release time jitter due to the kernel preemption is found to be fluctuating in the range of -94 and +93 microseconds in 3% of the cases. So, the kernel preemption time for the hard and mission critical real time systems is taken to be less than 99 microseconds. Whereas 85% of the cases have release time jitters of single digit only. So, the kernel preemption time in average case is considered to be less than 9 microseconds.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-125390913-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-125390913-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708414/figure-1-embedded-model-of-the-kernel-preemption-measurement"><img alt="Figure 1: Embedded Model of the Kernel Preemption Measurement. There is a device or a software simulator called the TRIANG that generates a triangular sweep wave [30]. Besides, ‘n’ numbers of tasks with real time priorities are scheduled. Both the waves and the jobs share the central memory as shown in Figure 1. Obviously, the timer is associated with all the tasks and also to the TRIANG. The system calls provided by the RTOS are used to activate timer C.... kt " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708417/figure-2-the-embedded-model-is-altered-bit-in-order-to"><img alt="The Embedded model is altered a bit in order to obtain the Software model as show in Figure 2. The triangular wave generator is absent here and the comparator is replaced with a software tool to which | have given the name “Time Measuring Tool” that could be one of the following: " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708421/figure-3-the-key-concept-of-the-model-is-that-it-measures"><img alt="The key concept of the model is that it measures the intervals of two consecutive periodic releases of the real time task - giving rise to the measurement of the release time jitters due to the kernel preemption delay. Essentially, the measurement is done for a several thousand releases of the task. The worst time is considered to be the required measurement. Between the two models, the software model was chosen for the experiment while another model is left as a separate research work. RTLinux was selected as the OS kernel due to its superiority among others [14]. " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708423/figure-4-all-the-collected-data-are-shown-with-the-help-of"><img alt="All the collected data are shown with the help of a line chart. A number of line charts are prepared with increasing number of data collected. All the line charts have data series of “preemption disabled” case also because of the intent of showing the interrupt blocking time as well. ll the collected data are shown with the help of a line chart. A number of line charts are prepared with increasing number of data Measurement of Kernel Preemption Time of Real Time Operating Systems " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708425/figure-1-the-central-ideal-straight-line-is-the-defined"><img alt="The central ideal straight line is the defined period showing the value of 10000 microseconds at each period. The dark line touching above the ideal line is the preemptible case and the most curved line represents the non-preemptible case. The graph shown by Figure 1 shows the 100 collected data. In here, the preemptible case has a uniformity and very less fluctuation at the ideal line at 10000. " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/figure_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708427/figure-6-discussion-measurement-of-kernel-preemption-time-of"><img alt="Discussion Measurement of Kernel Preemption Time of Real Time Operating Systems " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/figure_006.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708428/table-1-introduction-measurement-of-kernel-preemption-time"><img alt="Introduction Measurement of Kernel Preemption Time of Real Time Operating Systems A Real Time System is in general categorized into three types- Hard, Soft and Firm Real Time System according to the usefulness unction on the basis of their timing constraints i.e. deadline [1]. Hard RTS is subject to crash or may cause physical hazards upon 10t meeting the deadline. A mission critical system, for example, always has a hard deadline [2]. In order to schedule the tasks of the system, all parameters including temporal and resource parameters for task preemption should be known well in advance [3]. The orrectness of the timing measurements is vital in this regard because task should be executed exactly at the one time (considering the ask execution time and delays due to other overheads) in order to meet the deadline at the given time [4]. " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708429/table-4-release-time-jitters-categorically-broken-down-hence"><img alt="Table 4: Release Time Jitters categorically broken-down. Hence, it would be worth looking at the categorical distribution of the jitters measured. All jitters are grouped into 3 (three) cate- ories: - No jitters at all (0), One digit number (0-9) and two-digit number (10-99). Here is Table 3 showing the occurrences of jitters nd their percentage with respect the data collected. " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/table_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/42708430/table-3-categorical-distribution-of-release-time-jitters-it"><img alt="Table 3: Categorical distribution of release time jitters. It is interesting observation that the occurrence of the min and max jitters are only once each - contributing only 0.04% of the total. Table 2: Min and Max data observed from the experiment. " class="figure-slide-image" src="https://figures.academia-assets.com/119443361/table_003.jpg" /></a></figure></div><div class="next-slide-container js-next-button-container"><button aria-label="Next" class="carousel-navigation-button js-profile-work-125390913-figures-next"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_forward_ios</span></button></div></div></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="00297707cd1cddada30de6a8880eaeb4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119443361,"asset_id":125390913,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119443361/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="125390913"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="125390913"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 125390913; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=125390913]").text(description); $(".js-view-count[data-work-id=125390913]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 125390913; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='125390913']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "00297707cd1cddada30de6a8880eaeb4" } } $('.js-work-strip[data-work-id=125390913]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":125390913,"title":"Measurement of Kernel Preemption Time of Real Time Operating Systems","translated_title":"","metadata":{"ai_title_tag":"Kernel Preemption Time in Real Time Systems","grobid_abstract":"In a Real Time System, when a high priority task is scheduled, it preempts ongoing lower priority tasks and sometimes it preempts even the kernel too. 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The results from the experiments show that the release time jitter due to the kernel preemption is found to be fluctuating in the range of -94 and +93 microseconds in 3% of the cases. So, the kernel preemption time for the hard and mission critical real time systems is taken to be less than 99 microseconds. Whereas 85% of the cases have release time jitters of single digit only. 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In normal cases, the time needed for the preemption is too small and hence is not considered in task scheduling. But, in some mission critical hard real time systems, even a small fraction of a second should be taken into account as it could affect the deadline. This research work proposes two models -embedded model and software model, among which the latter has been used for the experiment that calculates the kernel preemption time. The hardware model consists of a triangular wave generator called TRIANG that takes timer values from real time tasks and sends outputs to a comparator module. Finally, the comparator compares the time differences, and hence, calculates the kernel preemption time. Whereas the software model uses the RT-Linux architecture in which device drivers are used instead of hardware components. A high precision timer clock is also used to measure the task release times. The results from the experiments show that the release time jitter due to the kernel preemption is found to be fluctuating in the range of -94 and +93 microseconds in 3% of the cases. So, the kernel preemption time for the hard and mission critical real time systems is taken to be less than 99 microseconds. Whereas 85% of the cases have release time jitters of single digit only. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (true) { Aedu.setUpFigureCarousel('profile-work-125390913-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="109494623"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/109494623/Computational_Social_Science"><img alt="Research paper thumbnail of Computational Social Science" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Computational Social Science</div><div class="wp-workCard_item"><span>Routledge eBooks</span><span>, Jun 9, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494623"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494623"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494623; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-109494623-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="109494622"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109494622/Comparison_of_Data_MigrationTechniques_from_SQL_Databaseto_NoSQL_Database"><img alt="Research paper thumbnail of Comparison of Data MigrationTechniques from SQL Databaseto NoSQL Database" class="work-thumbnail" src="https://attachments.academia-assets.com/107603551/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109494622/Comparison_of_Data_MigrationTechniques_from_SQL_Databaseto_NoSQL_Database">Comparison of Data MigrationTechniques from SQL Databaseto NoSQL Database</a></div><div class="wp-workCard_item"><span>Journal of computer engineering & information technology</span><span>, Dec 28, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">With rapid and multi-dimensional growth of data, Relational Database Management System (RDBMS) ha...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">With rapid and multi-dimensional growth of data, Relational Database Management System (RDBMS) having Structured Query Language (SQL) support is facing difficulties in managing huge data due to lack of dynamic data model, performance and scalability issues etc. NoSQL database addresses these issues by providing the features that SQL database lacks. So, many organizations are migrating from SQL to NoSQL. RDBMS database deals with structured data and NoSQL database with structured, unstructured and semi-structured data. As the continuous development of applications is taking place, a huge volume of data collected has already been taken for architectural migration from SQL database to NoSQL database. Since NoSQL is emerging and evolving technology in the field of database management and because of increased maturity of NoSQL database technology, many applications have already switched to NoSQL so that extracting information from big data. This study discusses, analyzes and compares 7 (seven) different techniques of data migration from SQL database to NoSQL database. The migration is performed by using appropriated tools / frameworks available for each technique and the results are evaluated, analyzed and validated using a system tool called SysGauge. The parameters used for the analysis and the comparison are Speed, Execution Time, Maximum CPU Usage and Maximum Memory Usage. At the end of the entire work, the most efficient techniques have been recommended.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0bb9a09f9da325b48c9fbac6b9c63496" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107603551,"asset_id":109494622,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107603551/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494622"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494622"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494622; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109494622]").text(description); $(".js-view-count[data-work-id=109494622]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109494622; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109494622']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "0bb9a09f9da325b48c9fbac6b9c63496" } } $('.js-work-strip[data-work-id=109494622]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109494622,"title":"Comparison of Data MigrationTechniques from SQL Databaseto NoSQL Database","translated_title":"","metadata":{"publisher":"OMICS Publishing Group","ai_title_tag":"Comparative Analysis of SQL to NoSQL Data Migration Techniques","grobid_abstract":"With rapid and multi-dimensional growth of data, Relational Database Management System (RDBMS) having Structured Query Language (SQL) support is facing difficulties in managing huge data due to lack of dynamic data model, performance and scalability issues etc. NoSQL database addresses these issues by providing the features that SQL database lacks. So, many organizations are migrating from SQL to NoSQL. RDBMS database deals with structured data and NoSQL database with structured, unstructured and semi-structured data. As the continuous development of applications is taking place, a huge volume of data collected has already been taken for architectural migration from SQL database to NoSQL database. Since NoSQL is emerging and evolving technology in the field of database management and because of increased maturity of NoSQL database technology, many applications have already switched to NoSQL so that extracting information from big data. This study discusses, analyzes and compares 7 (seven) different techniques of data migration from SQL database to NoSQL database. The migration is performed by using appropriated tools / frameworks available for each technique and the results are evaluated, analyzed and validated using a system tool called SysGauge. 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In this contex...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection&quot; algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494621"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494621"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494621; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109494621]").text(description); $(".js-view-count[data-work-id=109494621]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109494621; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109494621']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=109494621]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109494621,"title":"Hybrid Intrusion Detection","translated_title":"","metadata":{"abstract":"Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection\u0026quot; algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.","publisher":"LAP LAMBERT Academic Publishing","publication_date":{"day":14,"month":11,"year":2016,"errors":{}},"publication_name":"LAP LAMBERT Academic Publishing eBooks"},"translated_abstract":"Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection\u0026quot; algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.","internal_url":"https://www.academia.edu/109494621/Hybrid_Intrusion_Detection","translated_internal_url":"","created_at":"2023-11-20T19:27:11.566-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Hybrid_Intrusion_Detection","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection\u0026quot; algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.","owner":{"id":371695,"first_name":"Roshan","middle_initials":null,"last_name":"Chitrakar","page_name":"RoshanChitrakar","domain_name":"ncit","created_at":"2011-03-21T15:54:06.502-07:00","display_name":"Roshan Chitrakar","url":"https://ncit.academia.edu/RoshanChitrakar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining"},{"id":31349,"name":"Anomaly Detection","url":"https://www.academia.edu/Documents/in/Anomaly_Detection"},{"id":131237,"name":"Cluster Analysis","url":"https://www.academia.edu/Documents/in/Cluster_Analysis"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine"},{"id":320736,"name":"Intrusion Detection System","url":"https://www.academia.edu/Documents/in/Intrusion_Detection_System"}],"urls":[{"id":35622771,"url":"https://www.knigozal.com/store/gb/book/hybrid-intrusion-detection/isbn/978-3-659-97921-7"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-109494621-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="109494620"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/109494620/Selection_of_Candidate_Support_Vectors_in_incremental_SVM_for_network_intrusion_detection"><img alt="Research paper thumbnail of Selection of Candidate Support Vectors in incremental SVM for network intrusion detection" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Selection of Candidate Support Vectors in incremental SVM for network intrusion detection</div><div class="wp-workCard_item"><span>Computers & Security</span><span>, Sep 1, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as no...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494620"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494620"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494620; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109494620]").text(description); $(".js-view-count[data-work-id=109494620]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109494620; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109494620']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=109494620]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109494620,"title":"Selection of Candidate Support Vectors in incremental SVM for network intrusion detection","translated_title":"","metadata":{"abstract":"ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.","publisher":"Elsevier BV","publication_date":{"day":1,"month":9,"year":2014,"errors":{}},"publication_name":"Computers \u0026 Security"},"translated_abstract":"ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.","internal_url":"https://www.academia.edu/109494620/Selection_of_Candidate_Support_Vectors_in_incremental_SVM_for_network_intrusion_detection","translated_internal_url":"","created_at":"2023-11-20T19:27:11.157-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Selection_of_Candidate_Support_Vectors_in_incremental_SVM_for_network_intrusion_detection","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.","owner":{"id":371695,"first_name":"Roshan","middle_initials":null,"last_name":"Chitrakar","page_name":"RoshanChitrakar","domain_name":"ncit","created_at":"2011-03-21T15:54:06.502-07:00","display_name":"Roshan Chitrakar","url":"https://ncit.academia.edu/RoshanChitrakar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":3703,"name":"Network Security","url":"https://www.academia.edu/Documents/in/Network_Security"},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine"},{"id":320736,"name":"Intrusion Detection System","url":"https://www.academia.edu/Documents/in/Intrusion_Detection_System"},{"id":1164759,"name":"Computers Security","url":"https://www.academia.edu/Documents/in/Computers_Security"}],"urls":[{"id":35622770,"url":"https://doi.org/10.1016/j.cose.2014.06.006"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-109494620-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="109494619"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109494619/Hybrid_Approaches_to_Block_Cipher"><img alt="Research paper thumbnail of Hybrid Approaches to Block Cipher" class="work-thumbnail" src="https://attachments.academia-assets.com/107603495/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109494619/Hybrid_Approaches_to_Block_Cipher">Hybrid Approaches to Block Cipher</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This chapter introduces two new approaches to block cipher-one is DNA hybridization encryption sc...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This chapter introduces two new approaches to block cipher-one is DNA hybridization encryption scheme (DHES) and the other is hybrid graphical encryption algorithm (HGEA). DNA cryptography deals with the techniques of hiding messages in the form of a DNA sequence. The key size of data encryption standard (DES) can be increased by using DHES. In DHES, DNA cryptography algorithm is used for encryption and decryption, and one-time pad (OTP) scheme is used for key generation. The output of DES algorithm is passed as an input to DNA hybridization scheme to provide an added security. The second approach, HGEA, is based on graphical pattern recognition. By performing multiple transformations, shifting and logical operations, a block cipher is obtained. This algorithm is influenced by hybrid cubes encryption algorithm (HiSea). Features like graphical interpretation and computation of selected quadrant value are the unique features of HGEA. Moreover, multiple key generation scheme combined with graphical interpretation method provides an increased level of security.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-109494619-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-109494619-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353814/figure-1-hybrid-approaches-to-block-cipher"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/107603495/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353817/figure-2-ees-flow-chart-of-dna-hybridization-decryption"><img alt="Figure 2. a © ees Flow chart of DNA hybridization decryption. " class="figure-slide-image" src="https://figures.academia-assets.com/107603495/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353835/figure-3-key-this-is-because-the-length-of-ciphertext"><img alt="key; this is because the length of ciphertext depends upon the number of 1s present in the input plaintext. " class="figure-slide-image" src="https://figures.academia-assets.com/107603495/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353845/figure-4-this-encryption-algorithm-can-process-any-plaintext"><img alt="This encryption algorithm can process any “n” plaintext ASCII characters from input file. The input string is split into 8 bytes of m parts. Then, the input ASCII message bit is put up against the standard ASCII table. The plaintext value is then replaced by its ASCII value according to the table. This encryption encompasses numbers, special characters and even spaces. " class="figure-slide-image" src="https://figures.academia-assets.com/107603495/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353852/figure-5-hybrid-approaches-to-block-cipher"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/107603495/figure_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353861/figure-6-hybrid-approaches-to-block-cipher"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/107603495/figure_006.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353872/figure-5-file-size-vs-execution-time-of-des-and-hgea-the"><img alt="Figure 5. File size vs. execution time of DES and HGEA. The DDHO algorithm is tested on different types of plaintext; the encryption and decryption times are calculated; the analysis of length of plaintext, length of ciphertext and size of key is done and found that the length of ciphertext is pro- portional to the corresponding plaintext length. The encryption and decryption times increase slower with the changes in the length of plaintext. Y Y a From the process of analyzing various cryptographic algorithms, a unique encryption algorithm “hybrid graphical encryption algorithm” has been proposed. The algorithm was based on hybrid cubes encryption algorithm (HiSea). The fea- tures like graphical interpretation and computation of selected quadrant value are the unique features of this algorithm, which is different from existing standard " class="figure-slide-image" src="https://figures.academia-assets.com/107603495/figure_007.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353887/table-2-performance-of-ddho-with-plaintexts-of-different"><img alt="Table 2. Performance of DDHO with plaintexts of different lengths and contents Plaintext of different contents for DDHO algorithm. " class="figure-slide-image" src="https://figures.academia-assets.com/107603495/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353891/table-2-hybrid-approaches-to-block-cipher"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/107603495/table_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353908/table-3-hybrid-approaches-to-block-cipher"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/107603495/table_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353919/table-4-hybrid-approaches-to-block-cipher"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/107603495/table_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353939/table-5-ll-the-average-values-of-execution-time-for"><img alt="ll The average values of execution time for encryption and decryption were com- puted and are tabulated in Tables 12 and 13. " class="figure-slide-image" src="https://figures.academia-assets.com/107603495/table_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353956/table-6-test-results-hgea-execution-time-of-hges"><img alt="Table: Test results HGEA Execution time of HGES. " class="figure-slide-image" src="https://figures.academia-assets.com/107603495/table_006.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/45353965/table-7-hybrid-approaches-to-block-cipher"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/107603495/table_007.jpg" /></a></figure></div><div class="next-slide-container js-next-button-container"><button aria-label="Next" class="carousel-navigation-button js-profile-work-109494619-figures-next"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_forward_ios</span></button></div></div></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8bab95ca19a2cb10eb4a5e95039927c9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107603495,"asset_id":109494619,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107603495/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494619"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494619"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494619; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109494619]").text(description); $(".js-view-count[data-work-id=109494619]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109494619; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109494619']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "8bab95ca19a2cb10eb4a5e95039927c9" } } $('.js-work-strip[data-work-id=109494619]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109494619,"title":"Hybrid Approaches to Block Cipher","translated_title":"","metadata":{"grobid_abstract":"This chapter introduces two new approaches to block cipher-one is DNA hybridization encryption scheme (DHES) and the other is hybrid graphical encryption algorithm (HGEA). DNA cryptography deals with the techniques of hiding messages in the form of a DNA sequence. The key size of data encryption standard (DES) can be increased by using DHES. In DHES, DNA cryptography algorithm is used for encryption and decryption, and one-time pad (OTP) scheme is used for key generation. The output of DES algorithm is passed as an input to DNA hybridization scheme to provide an added security. The second approach, HGEA, is based on graphical pattern recognition. By performing multiple transformations, shifting and logical operations, a block cipher is obtained. This algorithm is influenced by hybrid cubes encryption algorithm (HiSea). Features like graphical interpretation and computation of selected quadrant value are the unique features of HGEA. Moreover, multiple key generation scheme combined with graphical interpretation method provides an increased level of security.","publication_date":{"day":10,"month":6,"year":2020,"errors":{}},"grobid_abstract_attachment_id":107603494},"translated_abstract":null,"internal_url":"https://www.academia.edu/109494619/Hybrid_Approaches_to_Block_Cipher","translated_internal_url":"","created_at":"2023-11-20T19:27:10.937-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":107603495,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107603495/thumbnails/1.jpg","file_name":"64945.pdf","download_url":"https://www.academia.edu/attachments/107603495/download_file","bulk_download_file_name":"Hybrid_Approaches_to_Block_Cipher.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107603495/64945-libre.pdf?1700539797=\u0026response-content-disposition=attachment%3B+filename%3DHybrid_Approaches_to_Block_Cipher.pdf\u0026Expires=1743458402\u0026Signature=GP5FWjYMZ3eNjV-A2bee4IJgjI8AKgQ2M4hQK9uG5QtsLo9s5XODpXI8nJpatnC9UZZ46TrIeoaulmiT-bFKNc38iDdT8kZOwUD9JuL8GzdXMLBhBhtVHw5Pj1noQB0Z2lcgAr73UdMhDkawwHWF-GmMpJlDByhZatl0VhAJPok8vDwfEm3akT~kGdyFx~DiTC-D8asx~QEcPoMaAyxDSaPxF0qaKNdRusn6uCBd7y~N~0TnZ7xZpvQAlkM3Dt6gTWi3Zp890ikuR5g-gevnuaexijMUX3HCzG27zTRGGN6TOej3JluL5Uig6TInPw3GH6e54KkZSqKvLuxxNqewHg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Hybrid_Approaches_to_Block_Cipher","translated_slug":"","page_count":22,"language":"en","content_type":"Work","summary":"This chapter introduces two new approaches to block cipher-one is DNA hybridization encryption scheme (DHES) and the other is hybrid graphical encryption algorithm (HGEA). DNA cryptography deals with the techniques of hiding messages in the form of a DNA sequence. The key size of data encryption standard (DES) can be increased by using DHES. In DHES, DNA cryptography algorithm is used for encryption and decryption, and one-time pad (OTP) scheme is used for key generation. The output of DES algorithm is passed as an input to DNA hybridization scheme to provide an added security. The second approach, HGEA, is based on graphical pattern recognition. By performing multiple transformations, shifting and logical operations, a block cipher is obtained. This algorithm is influenced by hybrid cubes encryption algorithm (HiSea). Features like graphical interpretation and computation of selected quadrant value are the unique features of HGEA. Moreover, multiple key generation scheme combined with graphical interpretation method provides an increased level of security.","owner":{"id":371695,"first_name":"Roshan","middle_initials":null,"last_name":"Chitrakar","page_name":"RoshanChitrakar","domain_name":"ncit","created_at":"2011-03-21T15:54:06.502-07:00","display_name":"Roshan Chitrakar","url":"https://ncit.academia.edu/RoshanChitrakar","email":"U0o3K0RRcTUrUjVOZ29wZHRiSUdOelB6RkxHcFZTK0VzQVdyU09TeFUwVT0tLWMwNHZUbnE3QzZ5anJNNEtxYVJWSXc9PQ==--19db7384772616dff5c9e6f0676089a437dfdc7d"},"attachments":[{"id":107603495,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107603495/thumbnails/1.jpg","file_name":"64945.pdf","download_url":"https://www.academia.edu/attachments/107603495/download_file","bulk_download_file_name":"Hybrid_Approaches_to_Block_Cipher.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107603495/64945-libre.pdf?1700539797=\u0026response-content-disposition=attachment%3B+filename%3DHybrid_Approaches_to_Block_Cipher.pdf\u0026Expires=1743458402\u0026Signature=GP5FWjYMZ3eNjV-A2bee4IJgjI8AKgQ2M4hQK9uG5QtsLo9s5XODpXI8nJpatnC9UZZ46TrIeoaulmiT-bFKNc38iDdT8kZOwUD9JuL8GzdXMLBhBhtVHw5Pj1noQB0Z2lcgAr73UdMhDkawwHWF-GmMpJlDByhZatl0VhAJPok8vDwfEm3akT~kGdyFx~DiTC-D8asx~QEcPoMaAyxDSaPxF0qaKNdRusn6uCBd7y~N~0TnZ7xZpvQAlkM3Dt6gTWi3Zp890ikuR5g-gevnuaexijMUX3HCzG27zTRGGN6TOej3JluL5Uig6TInPw3GH6e54KkZSqKvLuxxNqewHg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":107603494,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107603494/thumbnails/1.jpg","file_name":"64945.pdf","download_url":"https://www.academia.edu/attachments/107603494/download_file","bulk_download_file_name":"Hybrid_Approaches_to_Block_Cipher.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107603494/64945-libre.pdf?1700539797=\u0026response-content-disposition=attachment%3B+filename%3DHybrid_Approaches_to_Block_Cipher.pdf\u0026Expires=1743458402\u0026Signature=DP4m2j3jC5Q5WfCmtDCmN2dOey9lCmbHtDTW7hal~0A9pNR~r4ASylGksbBj-KSWts-~CMVP69GyC5VJnjooeorevVmSD-i-QKqTUvU-LP1EcykQmhXAmBLnslSqGpRJBuzZ2U~ELDHCvu1UvUTeb-C-1j8R6GjRLpyJRjSx1JCS4upsaXtokpRu6Hz4DoZ7MwqJw3Xz48Fnosx1z4UG4ZKwOD3BA1w3GkkTEqs-sKd6~oZPTx1oscs0wmagEmTKCU-DxcJ9ODtOBbSJE90wuY5HmLDVUbSxyXhzsiaxoafVP2jfM8n97WHjIkUNHM~-ZEyGhpuPv7G-XfGwO3QmCw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":48639,"name":"Computer and Network Security","url":"https://www.academia.edu/Documents/in/Computer_and_Network_Security"},{"id":790925,"name":"Block Cipher","url":"https://www.academia.edu/Documents/in/Block_Cipher"}],"urls":[{"id":35622769,"url":"https://www.intechopen.com/citation-pdf-url/64945"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (true) { Aedu.setUpFigureCarousel('profile-work-109494619-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="109494618"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/109494618/Anomaly_detection_using_Support_Vector_Machine_classification_with_k_Medoids_clustering"><img alt="Research paper thumbnail of Anomaly detection using Support Vector Machine classification with k-Medoids clustering" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Anomaly detection using Support Vector Machine classification with k-Medoids clustering</div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494618"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494618"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494618; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109494618]").text(description); $(".js-view-count[data-work-id=109494618]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109494618; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109494618']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=109494618]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109494618,"title":"Anomaly detection using Support Vector Machine classification with k-Medoids clustering","translated_title":"","metadata":{"abstract":"ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.","publication_date":{"day":1,"month":11,"year":2012,"errors":{}}},"translated_abstract":"ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.","internal_url":"https://www.academia.edu/109494618/Anomaly_detection_using_Support_Vector_Machine_classification_with_k_Medoids_clustering","translated_internal_url":"","created_at":"2023-11-20T19:27:10.751-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Anomaly_detection_using_Support_Vector_Machine_classification_with_k_Medoids_clustering","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.","owner":{"id":371695,"first_name":"Roshan","middle_initials":null,"last_name":"Chitrakar","page_name":"RoshanChitrakar","domain_name":"ncit","created_at":"2011-03-21T15:54:06.502-07:00","display_name":"Roshan Chitrakar","url":"https://ncit.academia.edu/RoshanChitrakar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining"},{"id":3703,"name":"Network Security","url":"https://www.academia.edu/Documents/in/Network_Security"},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines"},{"id":31349,"name":"Anomaly Detection","url":"https://www.academia.edu/Documents/in/Anomaly_Detection"},{"id":131237,"name":"Cluster Analysis","url":"https://www.academia.edu/Documents/in/Cluster_Analysis"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine"},{"id":315668,"name":"Svm","url":"https://www.academia.edu/Documents/in/Svm"},{"id":320736,"name":"Intrusion Detection System","url":"https://www.academia.edu/Documents/in/Intrusion_Detection_System"},{"id":795003,"name":"Linear Regression","url":"https://www.academia.edu/Documents/in/Linear_Regression"},{"id":999285,"name":"Handwritten Digit Classification","url":"https://www.academia.edu/Documents/in/Handwritten_Digit_Classification"},{"id":999290,"name":"Multi Class Classification","url":"https://www.academia.edu/Documents/in/Multi_Class_Classification"},{"id":1032327,"name":"False Positive Rate","url":"https://www.academia.edu/Documents/in/False_Positive_Rate"},{"id":1553450,"name":"Naive Bayes Classifier","url":"https://www.academia.edu/Documents/in/Naive_Bayes_Classifier"}],"urls":[{"id":35622768,"url":"https://doi.org/10.1109/ahici.2012.6408446"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-109494618-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="109494617"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/109494617/Anomaly_Based_Intrusion_Detection_Using_Hybrid_Learning_Approach_of_Combining_k_Medoids_Clustering_and_Na%C3%AFve_Bayes_Classification"><img alt="Research paper thumbnail of Anomaly Based Intrusion Detection Using Hybrid Learning Approach of Combining k-Medoids Clustering and Naïve Bayes Classification" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Anomaly Based Intrusion Detection Using Hybrid Learning Approach of Combining k-Medoids Clustering and Naïve Bayes Classification</div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Informat...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494617"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494617"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494617; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109494617]").text(description); $(".js-view-count[data-work-id=109494617]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109494617; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109494617']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=109494617]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109494617,"title":"Anomaly Based Intrusion Detection Using Hybrid Learning Approach of Combining k-Medoids Clustering and Naïve Bayes Classification","translated_title":"","metadata":{"abstract":"ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. 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An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.","internal_url":"https://www.academia.edu/109494617/Anomaly_Based_Intrusion_Detection_Using_Hybrid_Learning_Approach_of_Combining_k_Medoids_Clustering_and_Na%C3%AFve_Bayes_Classification","translated_internal_url":"","created_at":"2023-11-20T19:27:10.565-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Anomaly_Based_Intrusion_Detection_Using_Hybrid_Learning_Approach_of_Combining_k_Medoids_Clustering_and_Naïve_Bayes_Classification","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-109494617-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="109494616"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/109494616/Intrusion_Detection_Based_on_PCA_with_Improved_K_Means"><img alt="Research paper thumbnail of Intrusion Detection Based on PCA with Improved K-Means" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Intrusion Detection Based on PCA with Improved K-Means</div><div class="wp-workCard_item"><span>Springer eBooks</span><span>, 2022</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494616"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494616"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494616; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-109494616-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="109494614"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/109494614/Integration_of_Expectation_Maximization_using_Gaussian_Mixture_Models_and_Na%C3%AFve_Bayes_for_Intrusion_Detection"><img alt="Research paper thumbnail of Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection" class="work-thumbnail" src="https://attachments.academia-assets.com/107603550/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/109494614/Integration_of_Expectation_Maximization_using_Gaussian_Mixture_Models_and_Na%C3%AFve_Bayes_for_Intrusion_Detection">Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection</a></div><div class="wp-workCard_item"><span>Journal of computer science research</span><span>, Apr 20, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Intrusion detection is the investigation process of information about the system activities or it...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity. Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability. Nonetheless, it is Naïve use of the mean data value for the cluster core that presents a major drawback. The chances of two circular clusters having different radius and centering at the same mean will occur. This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together. However, if the clusters are not spherical, it fails. To overcome this issue, a new integrated hybrid model by integrating expectation maximizing (EM) clustering using a Gaussian mixture model (GMM) and naïve Bays classifier have been proposed. In this model, GMM give more flexibility than K-Means in terms of cluster covariance. Also, they use probabilities function and soft clustering, that's why they can have multiple cluster for a single data. In GMM, we can define the cluster form in GMM by two parameters: the mean and the standard deviation. This means that by using these two parameters, the cluster can take any kind of elliptical shape. EM-GMM will be used to cluster data based on data activity into the corresponding category.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="78a59d1a91329c816739240b8c066b40" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107603550,"asset_id":109494614,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107603550/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494614"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494614"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494614; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109494614]").text(description); $(".js-view-count[data-work-id=109494614]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109494614; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109494614']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "78a59d1a91329c816739240b8c066b40" } } $('.js-work-strip[data-work-id=109494614]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109494614,"title":"Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection","translated_title":"","metadata":{"publisher":"Bilingual Publishing Co.","ai_title_tag":"EM-GMM and Naïve Bayes for Intrusion Detection","grobid_abstract":"Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity. Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability. Nonetheless, it is Naïve use of the mean data value for the cluster core that presents a major drawback. The chances of two circular clusters having different radius and centering at the same mean will occur. This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together. However, if the clusters are not spherical, it fails. To overcome this issue, a new integrated hybrid model by integrating expectation maximizing (EM) clustering using a Gaussian mixture model (GMM) and naïve Bays classifier have been proposed. In this model, GMM give more flexibility than K-Means in terms of cluster covariance. Also, they use probabilities function and soft clustering, that's why they can have multiple cluster for a single data. In GMM, we can define the cluster form in GMM by two parameters: the mean and the standard deviation. This means that by using these two parameters, the cluster can take any kind of elliptical shape. EM-GMM will be used to cluster data based on data activity into the corresponding category.","publication_date":{"day":20,"month":4,"year":2021,"errors":{}},"publication_name":"Journal of computer science research","grobid_abstract_attachment_id":107603550},"translated_abstract":null,"internal_url":"https://www.academia.edu/109494614/Integration_of_Expectation_Maximization_using_Gaussian_Mixture_Models_and_Na%C3%AFve_Bayes_for_Intrusion_Detection","translated_internal_url":"","created_at":"2023-11-20T19:27:09.951-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":107603550,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107603550/thumbnails/1.jpg","file_name":"pdf.pdf","download_url":"https://www.academia.edu/attachments/107603550/download_file","bulk_download_file_name":"Integration_of_Expectation_Maximization.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107603550/pdf-libre.pdf?1700539790=\u0026response-content-disposition=attachment%3B+filename%3DIntegration_of_Expectation_Maximization.pdf\u0026Expires=1743492637\u0026Signature=IowNDcJWz-8Jb0XOsV5ybM8Bgx0hQOLSuBl9afMCUu3fL5DWJGaTNAclhYoma75T6XeeyGJguGRi-3w-icA9nVLF4XOdub5I343tsKzyHsNtV9xcIoEjTjATPx5zGs4TvPuR6iS-C3xceb~PnxHvAnC3rPBaqDAIqLNJ0j4gxbNvt64yXv~pFvaAIks9ZKBSoWf866GoQ0BSZxPIs8o5WZghq3OuKWr6ooNJ0yPtlqr~Nu6Ml~5HuxPuYrxkJTvy9zVEy6mE0iakLnXca~lXinOXILfNKiVKdMq~7jSlMNo~8INoMnVf5ePVD6I71vW6elBmQCUxXDENOy23LdJpdw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Integration_of_Expectation_Maximization_using_Gaussian_Mixture_Models_and_Naïve_Bayes_for_Intrusion_Detection","translated_slug":"","page_count":10,"language":"en","content_type":"Work","summary":"Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity. Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability. Nonetheless, it is Naïve use of the mean data value for the cluster core that presents a major drawback. The chances of two circular clusters having different radius and centering at the same mean will occur. This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together. However, if the clusters are not spherical, it fails. To overcome this issue, a new integrated hybrid model by integrating expectation maximizing (EM) clustering using a Gaussian mixture model (GMM) and naïve Bays classifier have been proposed. In this model, GMM give more flexibility than K-Means in terms of cluster covariance. Also, they use probabilities function and soft clustering, that's why they can have multiple cluster for a single data. In GMM, we can define the cluster form in GMM by two parameters: the mean and the standard deviation. This means that by using these two parameters, the cluster can take any kind of elliptical shape. 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The main objective of this paper is to propose a system that effectively detects DDoS attacks appearing in any networked system using the clustering technique of data mining followed by classification. This method uses a Heuristics Clustering Algorithm (HCA) to cluster the available data and Naïve Bayes (NB) classification to classify the data and detect the attacks created in the system based on some network attributes of the data packet. The clustering algorithm is based in unsupervised learning technique and is sometimes unable to detect some of the attack instances and few normal instances, therefore classification techniques are also used along with clustering to overcome this classification problem and to enhance the accuracy. Naïve Bayes classifiers are based on very strong independence assumptions with fairly simple construction to derive the conditional probability for each relationship. A series of experiment is performed using "The CAIDA UCSD DDoS Attack 2007 Dataset" and "DARPA 2000 Dataset" and the efficiency of the proposed system has been tested based on the following performance parameters: Accuracy, Detection Rate and False Positive Rate and the result obtained from the proposed system has been found that it has enhanced accuracy and detection rate with low false positive rate.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="60424b8af0b56da40bd13ebb106b4299" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107603501,"asset_id":109494479,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107603501/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="109494479"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="109494479"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 109494479; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=109494479]").text(description); $(".js-view-count[data-work-id=109494479]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 109494479; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='109494479']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "60424b8af0b56da40bd13ebb106b4299" } } $('.js-work-strip[data-work-id=109494479]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":109494479,"title":"DDoS Attack Detection Using Heuristics Clustering Algorithm and Na\u0026#239;ve Bayes Classification","translated_title":"","metadata":{"publisher":"Scientific Research Publishing","ai_title_tag":"DDoS Detection via Heuristic Clustering and Naïve Bayes","grobid_abstract":"In recent times among the multitude of attacks present in network system, DDoS attacks have emerged to be the attacks with the most devastating effects. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-91184838-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="79505191"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79505191/Physical_Health_Problems_and_Patterns_of_Self_Care_Associated_with_the_Use_of_Digital_Devices_among_University_Students"><img alt="Research paper thumbnail of Physical Health Problems and Patterns of Self-Care Associated with the Use of Digital Devices among University Students" class="work-thumbnail" src="https://attachments.academia-assets.com/86197456/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79505191/Physical_Health_Problems_and_Patterns_of_Self_Care_Associated_with_the_Use_of_Digital_Devices_among_University_Students">Physical Health Problems and Patterns of Self-Care Associated with the Use of Digital Devices among University Students</a></div><div class="wp-workCard_item"><span>MedS Alliance Journal of Medicine and Medical Sciences</span><span>, 2021</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">INTRODUCTION: Utilization of digital devices create some problems for users, such as, mental diso...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">INTRODUCTION: Utilization of digital devices create some problems for users, such as, mental disorder, visual problems, headache, weight gain and unnecessary time consumption. Therefore, this study aims to identify the information about the practices on the use of digital devices, its impact on physical health and pattern of self-care among the university students involved in different professions. MATERIALS AND METHODS: MPhil scholars involved in different professions (n= 315) of Nepal Open University (NOU) had participated in this cross-sectional online survey during January 2019 to August 2019. Multivariable analysis was employed to obtain rate ratios and chi-square test was used for the association of the use of digital devices with physical health problems. RESULTS: Socio-demographic factors like age was significantly associated with neck pain (p=0.02) and stiffness in hands/arms (p=0.04), while profession was associated with weight gain and difficulty in sleep (p=0.04). Moreov...</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-79505191-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-79505191-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/46328203/figure-1-took-rest-when-they-suffered-from-headache-neck"><img alt="took rest when they suffered from headache (54.2 %), neck pain (53.6 %), back pain (55.7 %), strain on hands and arms (59.4 %) and eye strain (49.1 %). Moreover, a higher proportion of participants had no treatment for sleep difficulty, passiveness of body and weight gain. Around one-fourth of the participants either took medicine or did meditation to get rid of physical health problems. Figure 1| Visualization of headache, eye problem, back pain, and neck pain and their treatment measures body and weight gain. Around one-fourth of the Patterns of treatment having a physical health problem among university students " class="figure-slide-image" src="https://figures.academia-assets.com/86197456/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/46328208/figure-2-visualization-of-train-hands-arms-sleep-difficulty"><img alt="Figure 2! Visualization of train hands/arms, sleep difficulty, passiveness of body and weight gain, and their " class="figure-slide-image" src="https://figures.academia-assets.com/86197456/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/46328211/table-1-socio-demographic-characteristics-and-the-use-of"><img alt="Table 1! Socio-demographic characteristics and the use of higher among respondents aged 41-60 years, whereas headache, back pain, strain on hands and arms, sleep difficulties, and body’s passiveness were higher among respondents aged 20-40 years. A significant association was found across socio- demographic characteristics and physical health problems like age group with neck pain (x2 = 5.081, p= 0.02, Phi=-0.13) and strain in hands/arms (x2 = 4.46, p= 0.04, Phi=0.13), profession with weight gain x2 = 4.2, p= 0.04, Phi=-0.12). In context to the profession, all types of physical health problems were higher among participants engaged in teaching profession however, weight gain was higher among participants who were involved in non-teaching profession (x2 = 4.19, p= 0.04, Phi=0.13). Likewise, sleep difficulty was greater among participants with work experience of less than ten years (x2 = 4.19, p= 0.04, Phi=0.13). However, weight gain was higher among participants with work experience of more than ten years (x2 = 4.57, p=0.03, Phi=-0.13). Moreover, the proportion of respondents with headache was higher among those who took tablets " class="figure-slide-image" src="https://figures.academia-assets.com/86197456/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/46328223/table-2-association-of-physical-health-problems-with-socio"><img alt="Table 2! Association of physical health problems with socio-demographic characteristics (n=315) " class="figure-slide-image" src="https://figures.academia-assets.com/86197456/table_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/46328236/table-3-physical-health-problems-and-patterns-of-self-care"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/86197456/table_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/46328251/table-4-between-the-remaining-physical-health-problems-with"><img alt="between the remaining physical health problems with the daily use of digital devices. " class="figure-slide-image" src="https://figures.academia-assets.com/86197456/table_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/46328262/table-5-pattern-of-treatment-having-physical-health-problems"><img alt="Table 5| Pattern of treatment having physical health problems among university students (n=315) " class="figure-slide-image" src="https://figures.academia-assets.com/86197456/table_005.jpg" /></a></figure></div><div class="next-slide-container js-next-button-container"><button aria-label="Next" class="carousel-navigation-button js-profile-work-79505191-figures-next"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_forward_ios</span></button></div></div></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="03abb542b3955d6647aff817b071727a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86197456,"asset_id":79505191,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86197456/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79505191"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79505191"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79505191; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79505191]").text(description); $(".js-view-count[data-work-id=79505191]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79505191; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79505191']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "03abb542b3955d6647aff817b071727a" } } $('.js-work-strip[data-work-id=79505191]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79505191,"title":"Physical Health Problems and Patterns of Self-Care Associated with the Use of Digital Devices among University Students","translated_title":"","metadata":{"abstract":"INTRODUCTION: Utilization of digital devices create some problems for users, such as, mental disorder, visual problems, headache, weight gain and unnecessary time consumption. 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Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79505190"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79505190"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79505190; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79505190]").text(description); $(".js-view-count[data-work-id=79505190]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79505190; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79505190']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=79505190]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79505190,"title":"Anomaly Based Intrusion Detection Using Hybrid Learning Approach of Combining k-Medoids Clustering and Na\u0026#239;ve Bayes Classification","translated_title":"","metadata":{"abstract":"ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2012,"errors":{}},"publication_name":"2012 8th International Conference on Wireless Communications, Networking and Mobile Computing"},"translated_abstract":"ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.","internal_url":"https://www.academia.edu/79505190/Anomaly_Based_Intrusion_Detection_Using_Hybrid_Learning_Approach_of_Combining_k_Medoids_Clustering_and_Na_and_239_ve_Bayes_Classification","translated_internal_url":"","created_at":"2022-05-20T03:01:50.091-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Anomaly_Based_Intrusion_Detection_Using_Hybrid_Learning_Approach_of_Combining_k_Medoids_Clustering_and_Na_and_239_ve_Bayes_Classification","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT The role of Intrusion Detection System (IDS) has been inevitable in the area of Information and Network Security - specially for building a good network defense infrastructure. Anomaly based intrusion detection technique is one of the building blocks of such a foundation. In this paper, the attempt has been made to apply hybrid learning approach by combining k-Medoids based clustering technique followed by Naïve Bayes classification technique. Because of the fact that k-Medoids clustering techniques represent the real world scenario of data distribution, the proposed enhanced approach will group the whole data into corresponding clusters more accurately than kMeans such that it results in a better classification. An experiment is carried out in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Results and analyses show that the proposed approach has enhanced.","owner":{"id":371695,"first_name":"Roshan","middle_initials":null,"last_name":"Chitrakar","page_name":"RoshanChitrakar","domain_name":"ncit","created_at":"2011-03-21T15:54:06.502-07:00","display_name":"Roshan Chitrakar","url":"https://ncit.academia.edu/RoshanChitrakar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":1283,"name":"Information Security","url":"https://www.academia.edu/Documents/in/Information_Security"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining"},{"id":3703,"name":"Network Security","url":"https://www.academia.edu/Documents/in/Network_Security"},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines"},{"id":131237,"name":"Cluster Analysis","url":"https://www.academia.edu/Documents/in/Cluster_Analysis"},{"id":315668,"name":"Svm","url":"https://www.academia.edu/Documents/in/Svm"},{"id":320736,"name":"Intrusion Detection System","url":"https://www.academia.edu/Documents/in/Intrusion_Detection_System"},{"id":795003,"name":"Linear Regression","url":"https://www.academia.edu/Documents/in/Linear_Regression"},{"id":999285,"name":"Handwritten Digit Classification","url":"https://www.academia.edu/Documents/in/Handwritten_Digit_Classification"},{"id":999290,"name":"Multi Class Classification","url":"https://www.academia.edu/Documents/in/Multi_Class_Classification"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-79505190-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="79505189"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/79505189/Anomaly_detection_using_Support_Vector_Machine_classification_with_k_Medoids_clustering"><img alt="Research paper thumbnail of Anomaly detection using Support Vector Machine classification with k-Medoids clustering" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Anomaly detection using Support Vector Machine classification with k-Medoids clustering</div><div class="wp-workCard_item"><span>2012 Third Asian Himalayas International Conference on Internet</span><span>, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79505189"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79505189"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79505189; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79505189]").text(description); $(".js-view-count[data-work-id=79505189]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79505189; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79505189']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=79505189]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79505189,"title":"Anomaly detection using Support Vector Machine classification with k-Medoids clustering","translated_title":"","metadata":{"abstract":"ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.","publisher":"IEEE","publication_date":{"day":null,"month":null,"year":2012,"errors":{}},"publication_name":"2012 Third Asian Himalayas International Conference on Internet"},"translated_abstract":"ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.","internal_url":"https://www.academia.edu/79505189/Anomaly_detection_using_Support_Vector_Machine_classification_with_k_Medoids_clustering","translated_internal_url":"","created_at":"2022-05-20T03:01:49.868-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":38271545,"work_id":79505189,"tagging_user_id":371695,"tagged_user_id":null,"co_author_invite_id":694255,"email":"h***h@whu.edu.cn","display_order":0,"name":"Chuanhe Huang","title":"Anomaly detection using Support Vector Machine classification with k-Medoids clustering"}],"downloadable_attachments":[],"slug":"Anomaly_detection_using_Support_Vector_Machine_classification_with_k_Medoids_clustering","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT Anomaly based Intrusion Detection System, in the recent years, has become more dependent on learning methods - specially on classifications schemes. To make the classification more accurate and effective, hybrid approaches of combining with clustering techniques are often introduced. In this paper, a better combination is proposed to address problems of the previously proposed hybrid approach of combining k-Means/k-Medoids clustering technique with Naïve Bayes classification. In this new approach, the need of large samples by the previous approach is reduced by using Support Vector Machine while maintaining the high quality clustering of k-Medoids. Simulations have been carried out by using Kyoto2006+ data sets in order to evaluate performance, accuracy, detection rate and false positive rate of the classification scheme. Experiments and analyses show that the new approach is better in increasing the detection rate as well as in decreasing the false positive rate.","owner":{"id":371695,"first_name":"Roshan","middle_initials":null,"last_name":"Chitrakar","page_name":"RoshanChitrakar","domain_name":"ncit","created_at":"2011-03-21T15:54:06.502-07:00","display_name":"Roshan Chitrakar","url":"https://ncit.academia.edu/RoshanChitrakar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":3703,"name":"Network Security","url":"https://www.academia.edu/Documents/in/Network_Security"},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines"},{"id":315668,"name":"Svm","url":"https://www.academia.edu/Documents/in/Svm"},{"id":795003,"name":"Linear Regression","url":"https://www.academia.edu/Documents/in/Linear_Regression"},{"id":999285,"name":"Handwritten Digit Classification","url":"https://www.academia.edu/Documents/in/Handwritten_Digit_Classification"},{"id":999290,"name":"Multi Class Classification","url":"https://www.academia.edu/Documents/in/Multi_Class_Classification"}],"urls":[{"id":20610048,"url":"http://xplorestaging.ieee.org/ielx5/6396223/6408322/06408446.pdf?arnumber=6408446"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-79505189-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="79505188"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/79505188/Selection_of_Candidate_Support_Vectors_in_incremental_SVM_for_network_intrusion_detection"><img alt="Research paper thumbnail of Selection of Candidate Support Vectors in incremental SVM for network intrusion detection" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Selection of Candidate Support Vectors in incremental SVM for network intrusion detection</div><div class="wp-workCard_item"><span>Computers &amp; Security</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as no...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79505188"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79505188"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79505188; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79505188]").text(description); $(".js-view-count[data-work-id=79505188]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79505188; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79505188']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=79505188]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79505188,"title":"Selection of Candidate Support Vectors in incremental SVM for network intrusion detection","translated_title":"","metadata":{"abstract":"ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Computers \u0026amp; Security"},"translated_abstract":"ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.","internal_url":"https://www.academia.edu/79505188/Selection_of_Candidate_Support_Vectors_in_incremental_SVM_for_network_intrusion_detection","translated_internal_url":"","created_at":"2022-05-20T03:01:49.499-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Selection_of_Candidate_Support_Vectors_in_incremental_SVM_for_network_intrusion_detection","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT In an Incremental Support Vector Machine classification, the data objects labelled as non-support vectors by the previous classification are re-used as training data in the next classification along with new data samples verified by Karush-Kuhn-Tucker (KKT) condition. This paper proposes Half-partition strategy of selecting and retaining non-support vectors of the current increment of classification - named as Candidate Support Vectors (CSV) - which are likely to become support vectors in the next increment of classification. This research work also designs an algorithm named the Candidate Support Vector based Incremental SVM (CSV-ISVM) algorithm that implements the proposed strategy and materializes the whole process of incremental SVM classification. This work also suggests modifications to the previously proposed concentric-ring method and reserved set strategy. Performance of the proposed method is evaluated with experiments and also by comparing it with other ISVM techniques. Experimental results and performance analyses show that the proposed algorithm CSV-ISVM is better than general ISVM classifications for real-time network intrusion detection.","owner":{"id":371695,"first_name":"Roshan","middle_initials":null,"last_name":"Chitrakar","page_name":"RoshanChitrakar","domain_name":"ncit","created_at":"2011-03-21T15:54:06.502-07:00","display_name":"Roshan Chitrakar","url":"https://ncit.academia.edu/RoshanChitrakar"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines"},{"id":320736,"name":"Intrusion Detection System","url":"https://www.academia.edu/Documents/in/Intrusion_Detection_System"},{"id":1164759,"name":"Computers Security","url":"https://www.academia.edu/Documents/in/Computers_Security"}],"urls":[{"id":20610047,"url":"https://api.elsevier.com/content/article/PII:S0167404814000996?httpAccept=text/xml"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-79505188-figures'); } }); </script> <div class="js-work-strip profile--work_container" data-work-id="79505153"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/79505153/DDoS_Attack_Detection_Using_Heuristics_Clustering_Algorithm_and_Na%C3%AFve_Bayes_Classification"><img alt="Research paper thumbnail of DDoS Attack Detection Using Heuristics Clustering Algorithm and Naïve Bayes Classification" class="work-thumbnail" src="https://attachments.academia-assets.com/86197460/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/79505153/DDoS_Attack_Detection_Using_Heuristics_Clustering_Algorithm_and_Na%C3%AFve_Bayes_Classification">DDoS Attack Detection Using Heuristics Clustering Algorithm and Naïve Bayes Classification</a></div><div class="wp-workCard_item"><span>Journal of Information Security</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In recent times among the multitude of attacks present in network system, DDoS attacks have emerg...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In recent times among the multitude of attacks present in network system, DDoS attacks have emerged to be the attacks with the most devastating effects. The main objective of this paper is to propose a system that effectively detects DDoS attacks appearing in any networked system using the clustering technique of data mining followed by classification. This method uses a Heuristics Clustering Algorithm (HCA) to cluster the available data and Naïve Bayes (NB) classification to classify the data and detect the attacks created in the system based on some network attributes of the data packet. The clustering algorithm is based in unsupervised learning technique and is sometimes unable to detect some of the attack instances and few normal instances, therefore classification techniques are also used along with clustering to overcome this classification problem and to enhance the accuracy. Naïve Bayes classifiers are based on very strong independence assumptions with fairly simple construction to derive the conditional probability for each relationship. A series of experiment is performed using "The CAIDA UCSD DDoS Attack 2007 Dataset" and "DARPA 2000 Dataset" and the efficiency of the proposed system has been tested based on the following performance parameters: Accuracy, Detection Rate and False Positive Rate and the result obtained from the proposed system has been found that it has enhanced accuracy and detection rate with low false positive rate.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-79505153-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-79505153-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553871/figure-1-system-block-diagram-processing-those-datasets-are"><img alt="Figure 1. System block diagram. processing, those datasets are fed into Heuristics Clustering Algorithm that results would ultimately result in wrong output. Once, the dataset is prepared after pre- tasets followed by the preprocessing of data to eliminate those data values that " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553884/figure-2-improvement-in-accuracy-with-hca-clustering"><img alt="Improvement in Accuracy with HCA Clustering followed by NB Classification Figure 2. Improvement in Accuracy with HCA Followed by NB Classification. " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553891/figure-3-improvement-in-detection-rate-with-hca-clustering"><img alt="Figure 3. Improvement in Detection Rate with HCA Clustering followed by NB Classif cation. " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553896/figure-4-improvement-in-false-positive-rate-with-hca"><img alt="Figure 4. Improvement in false positive rate with HCA clustering followed by NB classi- fication. " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553901/table-1-comparison-of-accuracy-in-caida-ucsd-ddos-attack"><img alt="Table 1. Comparison of accuracy in CAIDA UCSD DDoS attack 2007 dataset. " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553908/table-2-comparison-of-accuracy-in-darpa-dataset-comparison"><img alt="Table 2. Comparison of accuracy in DARPA 2000 dataset. Table 3. Comparison of Detection Rate in CAIDA UCSD DDoS Attack 2007 Dataset. " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/table_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553911/table-4-comparison-of-detection-rate-in-darpa-dataset"><img alt="Table 4. Comparison of detection rate in DARPA 2000 dataset. " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/table_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553917/table-4-ddos-attack-detection-using-heuristics-clustering"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/86197460/table_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553928/table-5-comparison-of-false-positive-rate-in-caida-ucsd-ddos"><img alt="Table 5. Comparison of false positive rate in CAIDA UCSD DDoS attack 2007. Table 6. Comparison of false positive rate in DARPA 2000 dataset. " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/table_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/27553936/table-6-ddos-attack-detection-using-heuristics-clustering"><img alt="Performance Analysis " class="figure-slide-image" src="https://figures.academia-assets.com/86197460/table_006.jpg" /></a></figure></div><div class="next-slide-container js-next-button-container"><button aria-label="Next" class="carousel-navigation-button js-profile-work-79505153-figures-next"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_forward_ios</span></button></div></div></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="76e8a9429a90beabb2fc6a11597865f1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86197460,"asset_id":79505153,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86197460/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="79505153"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="79505153"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 79505153; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=79505153]").text(description); $(".js-view-count[data-work-id=79505153]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 79505153; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='79505153']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "76e8a9429a90beabb2fc6a11597865f1" } } $('.js-work-strip[data-work-id=79505153]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":79505153,"title":"DDoS Attack Detection Using Heuristics Clustering Algorithm and Naïve Bayes Classification","translated_title":"","metadata":{"publisher":"Scientific Research Publishing, Inc,","grobid_abstract":"In recent times among the multitude of attacks present in network system, DDoS attacks have emerged to be the attacks with the most devastating effects. 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The main objective of this paper is to propose a system that effectively detects DDoS attacks appearing in any networked system using the clustering technique of data mining followed by classification. This method uses a Heuristics Clustering Algorithm (HCA) to cluster the available data and Naïve Bayes (NB) classification to classify the data and detect the attacks created in the system based on some network attributes of the data packet. The clustering algorithm is based in unsupervised learning technique and is sometimes unable to detect some of the attack instances and few normal instances, therefore classification techniques are also used along with clustering to overcome this classification problem and to enhance the accuracy. Naïve Bayes classifiers are based on very strong independence assumptions with fairly simple construction to derive the conditional probability for each relationship. 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In this contex...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection" algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30232142"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30232142"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30232142; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30232142]").text(description); $(".js-view-count[data-work-id=30232142]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30232142; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30232142']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=30232142]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30232142,"title":"Hybrid Intrusion Detection : Clustering-Outlier and Incremental SVM","translated_title":"","metadata":{"abstract":"Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection\" algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference."},"translated_abstract":"Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection\" algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.","internal_url":"https://www.academia.edu/30232142/Hybrid_Intrusion_Detection_Clustering_Outlier_and_Incremental_SVM","translated_internal_url":"","created_at":"2016-12-03T06:20:53.979-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"book","co_author_tags":[{"id":26360425,"work_id":30232142,"tagging_user_id":371695,"tagged_user_id":null,"co_author_invite_id":694255,"email":"h***h@whu.edu.cn","display_order":0,"name":"Huang Chuanhe","title":"Hybrid Intrusion Detection : Clustering-Outlier and Incremental SVM"}],"downloadable_attachments":[],"slug":"Hybrid_Intrusion_Detection_Clustering_Outlier_and_Incremental_SVM","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection\" algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.","owner":{"id":371695,"first_name":"Roshan","middle_initials":null,"last_name":"Chitrakar","page_name":"RoshanChitrakar","domain_name":"ncit","created_at":"2011-03-21T15:54:06.502-07:00","display_name":"Roshan Chitrakar","url":"https://ncit.academia.edu/RoshanChitrakar"},"attachments":[],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining"},{"id":69540,"name":"Intrusion Detection","url":"https://www.academia.edu/Documents/in/Intrusion_Detection"}],"urls":[{"id":7790001,"url":"https://www.morebooks.de/bookprice_offer_f38b7f9df15ac72505fd5d180dc2a9a7ae62036f?auth_token=d3d3LmxhcC1wdWJsaXNoaW5nLmNvbTpiYWExYzc2Nzc0NjVjNGFhZjg0NWI2MDY2M2NkZWMwYg==\u0026locale=gb\u0026currency=EUR"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") if (false) { Aedu.setUpFigureCarousel('profile-work-30232142-figures'); } }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="6341474" id="thesischapters"><div class="js-work-strip profile--work_container" data-work-id="30659598"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/30659598/Hybr%D1%96d_%D0%86ntrus%D1%96%D0%BEn_D%D0%B5t%D0%B5ct%D1%96%D0%BEn_w%D1%96th_Clust%D0%B5r%D1%96ng_%D0%9Eutl%D1%96%D0%B5r_T%D0%B5chn%D1%96qu%D0%B5_%D0%B0nd_%D0%86ncr%D0%B5m%D0%B5nt%D0%B0l_SVM_Cl%D0%B0ss%D1%96f%D1%96c%D0%B0t%D1%96%D0%BEn"><img alt="Research paper thumbnail of Hybrіd Іntrusіоn Dеtеctіоn wіth Clustеrіng-Оutlіеr Tеchnіquе аnd Іncrеmеntаl SVM Clаssіfіcаtіоn" class="work-thumbnail" src="https://attachments.academia-assets.com/51102629/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/30659598/Hybr%D1%96d_%D0%86ntrus%D1%96%D0%BEn_D%D0%B5t%D0%B5ct%D1%96%D0%BEn_w%D1%96th_Clust%D0%B5r%D1%96ng_%D0%9Eutl%D1%96%D0%B5r_T%D0%B5chn%D1%96qu%D0%B5_%D0%B0nd_%D0%86ncr%D0%B5m%D0%B5nt%D0%B0l_SVM_Cl%D0%B0ss%D1%96f%D1%96c%D0%B0t%D1%96%D0%BEn">Hybrіd Іntrusіоn Dеtеctіоn wіth Clustеrіng-Оutlіеr Tеchnіquе аnd Іncrеmеntаl SVM Clаssіfіcаtіоn</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">With the rapid and wide-spread growth of internet technology, security risks and threats are also...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">With the rapid and wide-spread growth of internet technology, security risks and<br />threats are also increasing day by day. Newer versions of attacks and intrusions are<br />evolving continuously by putting extra challenges to the field of intrusion detection.<br />In this present context, this thesis work proposes a hybrid approach of intrusion<br />detection along with a hybrid architecture of intrusion detection system. The proposed<br />architecture is flexible enough to perform intrusion detection tasks either by using a<br />single hybrid module or by using multiple hybrid modules. The “Clustering-Outlier<br />detection followed by SVM classification” is proposed as the first hybrid IDS module<br />to be used in the architecture, whereas the second module proposed is the<br />“Incremental SVM with Half-partition method”. Аll thе prоpоsеd аpprоаchеs аnd rеsеаrch wоrks hаvе еnhаncеd thе dеtеctіоn rаtе<br />wіth mіnіmum fаlsе pоsіtіvе rаtеs. Thе prоpоsеd аlgоrіthms е.g. Clustеrіng-Оutlіеr<br />Dеtеctіоn аlgоrіthm аnd CSV-ІSVM аrе аlsо tеstеd аnd cоmpаrеd еxpеrіmеntаlly<br />wіth оthеr sіmіlаr mеthоds аnd аrе fоund bеttеr tо bе usеd by ІDS іn rеаl-tіmе<br />еnvіrоnmеnt. Thеsе prоpоsеd mеthоds cаn bе usеd fоr nеtwоrk іntrusіоn dеtеctіоn іn<br />rеаl-tіmе bеcаusе оf іts hіghеr dеtеctіоn rаtе, іmprоvеd fаlsе аlаrm rаtе аs wеll аs<br />аccеptаbly lеss аmоunt оf lеаrnіng tіmе.<br />Kеywоrds: Hybrіd Іntrusіоn Dеtеctіоn, Clustеrіng-Оutlіеr Dеtеctіоn, Іncrеmеntаl<br />SVM, Cаndіdаtе Suppоrt Vеctоr, Hаlf-Pаrtіtіоn Mеthоd.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2b7aadbc0dd9cb10e9635a3f5c86a50a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":51102629,"asset_id":30659598,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/51102629/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="30659598"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="30659598"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 30659598; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=30659598]").text(description); $(".js-view-count[data-work-id=30659598]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 30659598; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='30659598']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "2b7aadbc0dd9cb10e9635a3f5c86a50a" } } $('.js-work-strip[data-work-id=30659598]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":30659598,"title":"Hybrіd Іntrusіоn Dеtеctіоn wіth Clustеrіng-Оutlіеr Tеchnіquе аnd Іncrеmеntаl SVM Clаssіfіcаtіоn","translated_title":"","metadata":{"abstract":"With the rapid and wide-spread growth of internet technology, security risks and\nthreats are also increasing day by day. Newer versions of attacks and intrusions are\nevolving continuously by putting extra challenges to the field of intrusion detection.\nIn this present context, this thesis work proposes a hybrid approach of intrusion\ndetection along with a hybrid architecture of intrusion detection system. The proposed\narchitecture is flexible enough to perform intrusion detection tasks either by using a\nsingle hybrid module or by using multiple hybrid modules. The “Clustering-Outlier\ndetection followed by SVM classification” is proposed as the first hybrid IDS module\nto be used in the architecture, whereas the second module proposed is the\n“Incremental SVM with Half-partition method”. Аll thе prоpоsеd аpprоаchеs аnd rеsеаrch wоrks hаvе еnhаncеd thе dеtеctіоn rаtе\nwіth mіnіmum fаlsе pоsіtіvе rаtеs. Thе prоpоsеd аlgоrіthms е.g. Clustеrіng-Оutlіеr\nDеtеctіоn аlgоrіthm аnd CSV-ІSVM аrе аlsо tеstеd аnd cоmpаrеd еxpеrіmеntаlly\nwіth оthеr sіmіlаr mеthоds аnd аrе fоund bеttеr tо bе usеd by ІDS іn rеаl-tіmе\nеnvіrоnmеnt. Thеsе prоpоsеd mеthоds cаn bе usеd fоr nеtwоrk іntrusіоn dеtеctіоn іn\nrеаl-tіmе bеcаusе оf іts hіghеr dеtеctіоn rаtе, іmprоvеd fаlsе аlаrm rаtе аs wеll аs\nаccеptаbly lеss аmоunt оf lеаrnіng tіmе.\nKеywоrds: Hybrіd Іntrusіоn Dеtеctіоn, Clustеrіng-Оutlіеr Dеtеctіоn, Іncrеmеntаl\nSVM, Cаndіdаtе Suppоrt Vеctоr, Hаlf-Pаrtіtіоn Mеthоd."},"translated_abstract":"With the rapid and wide-spread growth of internet technology, security risks and\nthreats are also increasing day by day. Newer versions of attacks and intrusions are\nevolving continuously by putting extra challenges to the field of intrusion detection.\nIn this present context, this thesis work proposes a hybrid approach of intrusion\ndetection along with a hybrid architecture of intrusion detection system. The proposed\narchitecture is flexible enough to perform intrusion detection tasks either by using a\nsingle hybrid module or by using multiple hybrid modules. The “Clustering-Outlier\ndetection followed by SVM classification” is proposed as the first hybrid IDS module\nto be used in the architecture, whereas the second module proposed is the\n“Incremental SVM with Half-partition method”. Аll thе prоpоsеd аpprоаchеs аnd rеsеаrch wоrks hаvе еnhаncеd thе dеtеctіоn rаtе\nwіth mіnіmum fаlsе pоsіtіvе rаtеs. Thе prоpоsеd аlgоrіthms е.g. Clustеrіng-Оutlіеr\nDеtеctіоn аlgоrіthm аnd CSV-ІSVM аrе аlsо tеstеd аnd cоmpаrеd еxpеrіmеntаlly\nwіth оthеr sіmіlаr mеthоds аnd аrе fоund bеttеr tо bе usеd by ІDS іn rеаl-tіmе\nеnvіrоnmеnt. Thеsе prоpоsеd mеthоds cаn bе usеd fоr nеtwоrk іntrusіоn dеtеctіоn іn\nrеаl-tіmе bеcаusе оf іts hіghеr dеtеctіоn rаtе, іmprоvеd fаlsе аlаrm rаtе аs wеll аs\nаccеptаbly lеss аmоunt оf lеаrnіng tіmе.\nKеywоrds: Hybrіd Іntrusіоn Dеtеctіоn, Clustеrіng-Оutlіеr Dеtеctіоn, Іncrеmеntаl\nSVM, Cаndіdаtе Suppоrt Vеctоr, Hаlf-Pаrtіtіоn Mеthоd.","internal_url":"https://www.academia.edu/30659598/Hybr%D1%96d_%D0%86ntrus%D1%96%D0%BEn_D%D0%B5t%D0%B5ct%D1%96%D0%BEn_w%D1%96th_Clust%D0%B5r%D1%96ng_%D0%9Eutl%D1%96%D0%B5r_T%D0%B5chn%D1%96qu%D0%B5_%D0%B0nd_%D0%86ncr%D0%B5m%D0%B5nt%D0%B0l_SVM_Cl%D0%B0ss%D1%96f%D1%96c%D0%B0t%D1%96%D0%BEn","translated_internal_url":"","created_at":"2016-12-29T01:41:39.910-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":371695,"coauthors_can_edit":true,"document_type":"thesis_chapter","co_author_tags":[{"id":26818337,"work_id":30659598,"tagging_user_id":371695,"tagged_user_id":null,"co_author_invite_id":694255,"email":"h***h@whu.edu.cn","display_order":1,"name":"Huang Chuanhe","title":"Hybrіd Іntrusіоn Dеtеctіоn wіth Clustеrіng-Оutlіеr Tеchnіquе аnd Іncrеmеntаl SVM Clаssіfіcаtіоn"}],"downloadable_attachments":[{"id":51102629,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/51102629/thumbnails/1.jpg","file_name":"Roshan2010172110001_Library_ToPrint.pdf","download_url":"https://www.academia.edu/attachments/51102629/download_file","bulk_download_file_name":"Hybrd_ntrusn_Dtctn_wth_Clustr.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/51102629/Roshan2010172110001_Library_ToPrint-libre.pdf?1483004675=\u0026response-content-disposition=attachment%3B+filename%3DHybrd_ntrusn_Dtctn_wth_Clustr.pdf\u0026Expires=1743492637\u0026Signature=cBOEjjeuvrjOtdqfrM5qiQnNZZ2oOoytTxg0dRqa5tuXucvqjpOChuvcLBe046eLe-H1uIja58Q4oadf0tJr9toJR6DxB9m2x-Do0l3d1Yb3IHfQP4ZDandkRoyrWKrnqA-rOg7t8ugGN9oQhhp~RAnmM5PFBD5O3Id8pwyzJeoDfUenviF6fpF5duKh3FnCkn9F~1IsUj5dUBRfICh3cE45vO0RQHOCEntefIF1pBJQdlwi2ysGhKGC~~mMTJaJ2pHN6XLrk~ZdTgB5eGtf7y1LAwFT9-8fFKFzenkWgdhvl84DftgQonXQMVvSrijOpyZP7qXmv-16ikd2beOmfQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Hybrіd_Іntrusіоn_Dеtеctіоn_wіth_Clustеrіng_Оutlіеr_Tеchnіquе_аnd_Іncrеmеntаl_SVM_Clаssіfіcаtіоn","translated_slug":"","page_count":145,"language":"en","content_type":"Work","summary":"With the rapid and wide-spread growth of internet technology, security risks and\nthreats are also increasing day by day. 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