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height="200" src="https://0.academia-photos.com/250413/5109414/5856347/s200_paul.tobin.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://dit.academia.edu/PaulTobin">Paul Tobin</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Dublin Institute of Technology</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://ncit.academia.edu/RoshanChitrakar"><img class="profile-avatar u-positionAbsolute" alt="Roshan Chitrakar" 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" src="https://0.academia-photos.com/371695/9733675/15833098/s200_roshan.chitrakar.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://ncit.academia.edu/RoshanChitrakar">Roshan Chitrakar</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Nepal College of Information Technology</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://uc-cl.academia.edu/JoseRagas"><img class="profile-avatar u-positionAbsolute" alt="Jose Ragas" 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" src="https://0.academia-photos.com/892321/326480/10860665/s200_jose.ragas.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://uc-cl.academia.edu/JoseRagas">Jose Ragas</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Pontificia Universidad Catolica de Chile</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://uerj.academia.edu/Jos%C3%A9Pessanha"><img class="profile-avatar u-positionAbsolute" alt="José Francisco Pessanha" 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" src="https://0.academia-photos.com/1957474/2757869/14606900/s200_jos_francisco.pessanha.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://uerj.academia.edu/Jos%C3%A9Pessanha">José Francisco Pessanha</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">UERJ - Universidade do Estado do Rio de Janeiro / Rio de Janeiro State University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://uts.academia.edu/BogdanGabrys"><img class="profile-avatar u-positionAbsolute" alt="Bogdan Gabrys" 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" src="https://0.academia-photos.com/2823240/923115/1155566/s200_bogdan.gabrys.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://uts.academia.edu/BogdanGabrys">Bogdan Gabrys</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of Technology Sydney</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://trans-techresearch.academia.edu/JiJianChin"><img class="profile-avatar u-positionAbsolute" alt="Ji-Jian Chin" 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" src="https://0.academia-photos.com/6771929/2639956/3069434/s200_jyan.chin.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://trans-techresearch.academia.edu/JiJianChin">Ji-Jian Chin</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University Of Plymouth</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://concordia.academia.edu/YogendraChaubey"><img class="profile-avatar u-positionAbsolute" alt="Yogendra Chaubey" 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" src="https://0.academia-photos.com/6982476/3366523/3961216/s200_yogendra.chaubey.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://concordia.academia.edu/YogendraChaubey">Yogendra Chaubey</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Concordia University (Canada)</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://kuk.academia.edu/SachinGupta"><img class="profile-avatar u-positionAbsolute" alt="Sachin Gupta" 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" src="https://0.academia-photos.com/8422633/2848539/7245195/s200_sachin.gupta.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://kuk.academia.edu/SachinGupta">Sachin Gupta</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Kurukshetra University</p></div></div></ul></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="31955997" href="https://www.academia.edu/Documents/in/Computer_Security"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://syr.academia.edu/VirPhoha","location":"/VirPhoha","scheme":"https","host":"syr.academia.edu","port":null,"pathname":"/VirPhoha","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Computer Security"]}" data-trace="false" data-dom-id="Pill-react-component-0b83ce4c-ec90-4c97-98db-16c5bb4bc92e"></div> <div id="Pill-react-component-0b83ce4c-ec90-4c97-98db-16c5bb4bc92e"></div> </a></div></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>269</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="Research-Articles" data-toggle="tab" href="#researcharticles" role="tab" title="Research Articles"><span>2</span> <span class="ds2-5-body-sm-bold">Research Articles</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 Vir Phoha</h3></div><div class="js-work-strip profile--work_container" data-work-id="117458193"><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/117458193/Resource_Management_for_Uninterrupted_Microgrid_Operation"><img alt="Research paper thumbnail of Resource Management for Uninterrupted Microgrid Operation" 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"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/117458193/Resource_Management_for_Uninterrupted_Microgrid_Operation">Resource Management for Uninterrupted Microgrid Operation</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">To meet the ever-increasing demand of electric power, microgrids are establishing themselves to b...</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">To meet the ever-increasing demand of electric power, microgrids are establishing themselves to be one of the most reliable power delivery systems. Microgrids can deliver power efficiently, cost-effectively and environment-friendly. To design a sustainable microgrid; several tasks such as resource allocation, optimization, power flow analyses, load demand analysis, cost analysis, etc., needs to be performed for a sustainable operation. The system should be able to process periodically a plethora of data originating from various sensors and measurement units. The system needs to process the data and then deliver appropriate control signals when some changes in the system occurs. Cloud computing can be an efficient way to handle this huge data processing task to meet various microgrid operational needs. It will also eliminate the need for a local macro server and thus will reduce the overall cost of the microgrid operation.</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="117458193"><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="117458193"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458193; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117458193]").text(description); $(".js-view-count[data-work-id=117458193]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117458193; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117458193']"); 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" } } $('.js-work-strip[data-work-id=117458193]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117458193,"title":"Resource Management for Uninterrupted Microgrid Operation","translated_title":"","metadata":{"abstract":"To meet the ever-increasing demand of electric power, microgrids are establishing themselves to be one of the most reliable power delivery systems. Microgrids can deliver power efficiently, cost-effectively and environment-friendly. To design a sustainable microgrid; several tasks such as resource allocation, optimization, power flow analyses, load demand analysis, cost analysis, etc., needs to be performed for a sustainable operation. The system should be able to process periodically a plethora of data originating from various sensors and measurement units. The system needs to process the data and then deliver appropriate control signals when some changes in the system occurs. Cloud computing can be an efficient way to handle this huge data processing task to meet various microgrid operational needs. It will also eliminate the need for a local macro server and thus will reduce the overall cost of the microgrid operation.","publication_date":{"day":1,"month":8,"year":2019,"errors":{}}},"translated_abstract":"To meet the ever-increasing demand of electric power, microgrids are establishing themselves to be one of the most reliable power delivery systems. Microgrids can deliver power efficiently, cost-effectively and environment-friendly. To design a sustainable microgrid; several tasks such as resource allocation, optimization, power flow analyses, load demand analysis, cost analysis, etc., needs to be performed for a sustainable operation. The system should be able to process periodically a plethora of data originating from various sensors and measurement units. The system needs to process the data and then deliver appropriate control signals when some changes in the system occurs. Cloud computing can be an efficient way to handle this huge data processing task to meet various microgrid operational needs. It will also eliminate the need for a local macro server and thus will reduce the overall cost of the microgrid operation.","internal_url":"https://www.academia.edu/117458193/Resource_Management_for_Uninterrupted_Microgrid_Operation","translated_internal_url":"","created_at":"2024-04-13T20:44:36.714-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Resource_Management_for_Uninterrupted_Microgrid_Operation","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"To meet the ever-increasing demand of electric power, microgrids are establishing themselves to be one of the most reliable power delivery systems. Microgrids can deliver power efficiently, cost-effectively and environment-friendly. To design a sustainable microgrid; several tasks such as resource allocation, optimization, power flow analyses, load demand analysis, cost analysis, etc., needs to be performed for a sustainable operation. The system should be able to process periodically a plethora of data originating from various sensors and measurement units. The system needs to process the data and then deliver appropriate control signals when some changes in the system occurs. Cloud computing can be an efficient way to handle this huge data processing task to meet various microgrid operational needs. It will also eliminate the need for a local macro server and thus will reduce the overall cost of the microgrid operation.","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":38643,"name":"Microgrid","url":"https://www.academia.edu/Documents/in/Microgrid"}],"urls":[{"id":41074165,"url":"https://doi.org/10.1109/sege.2019.8859865"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="117458192"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/117458192/The_Case_for_Contextually_Driven_Computation"><img alt="Research paper thumbnail of The Case for Contextually Driven Computation" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/117458192/The_Case_for_Contextually_Driven_Computation">The Case for Contextually Driven Computation</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="117458192"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="117458192"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458192; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117458192]").text(description); $(".js-view-count[data-work-id=117458192]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117458192; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117458192']"); 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" } } $('.js-work-strip[data-work-id=117458192]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117458192,"title":"The Case for Contextually Driven Computation","translated_title":"","metadata":{"publication_date":{"day":6,"month":12,"year":2010,"errors":{}}},"translated_abstract":null,"internal_url":"https://www.academia.edu/117458192/The_Case_for_Contextually_Driven_Computation","translated_internal_url":"","created_at":"2024-04-13T20:44:35.650-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"The_Case_for_Contextually_Driven_Computation","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":null,"owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"}],"urls":[{"id":41074164,"url":"https://doi.org/10.1201/b10398-2"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="117458191"><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/117458191/_title_Image_recovery_and_segmentation_using_competitive_learning_in_a_layered_network_title_"><img alt="Research paper thumbnail of <title>Image recovery and segmentation using competitive learning in a layered network</title>" 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"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/117458191/_title_Image_recovery_and_segmentation_using_competitive_learning_in_a_layered_network_title_"><title>Image recovery and segmentation using competitive learning in a layered network</title></a></div><div class="wp-workCard_item"><span>Proceedings of SPIE</span><span>, Oct 29, 1993</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this study, the principle of competitive learning is used to develop an iterative algorithm fo...</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 this study, the principle of competitive learning is used to develop an iterative algorithm for image recovery and segmentation. Within the framework of Markov Random Fields, the image recovery problem is transformed to the problem of minimization of an energy function. A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. Relationship of the update rule to Kohonen&#39;s update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. Simulation experiments using this algorithm on real and synthetic images show promising results on smoothing within regions and also on enhancing the boundaries. Restoration results computer favorably with recently published results using Markov Random Fields and mean field approximation.</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="117458191"><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="117458191"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458191; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117458191]").text(description); $(".js-view-count[data-work-id=117458191]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117458191; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117458191']"); 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" } } $('.js-work-strip[data-work-id=117458191]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117458191,"title":"\u003ctitle\u003eImage recovery and segmentation using competitive learning in a layered network\u003c/title\u003e","translated_title":"","metadata":{"abstract":"In this study, the principle of competitive learning is used to develop an iterative algorithm for image recovery and segmentation. Within the framework of Markov Random Fields, the image recovery problem is transformed to the problem of minimization of an energy function. A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. Relationship of the update rule to Kohonen\u0026#39;s update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. Simulation experiments using this algorithm on real and synthetic images show promising results on smoothing within regions and also on enhancing the boundaries. Restoration results computer favorably with recently published results using Markov Random Fields and mean field approximation.","publisher":"SPIE","publication_date":{"day":29,"month":10,"year":1993,"errors":{}},"publication_name":"Proceedings of SPIE"},"translated_abstract":"In this study, the principle of competitive learning is used to develop an iterative algorithm for image recovery and segmentation. Within the framework of Markov Random Fields, the image recovery problem is transformed to the problem of minimization of an energy function. A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. Relationship of the update rule to Kohonen\u0026#39;s update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. Simulation experiments using this algorithm on real and synthetic images show promising results on smoothing within regions and also on enhancing the boundaries. Restoration results computer favorably with recently published results using Markov Random Fields and mean field approximation.","internal_url":"https://www.academia.edu/117458191/_title_Image_recovery_and_segmentation_using_competitive_learning_in_a_layered_network_title_","translated_internal_url":"","created_at":"2024-04-13T20:44:35.469-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"_title_Image_recovery_and_segmentation_using_competitive_learning_in_a_layered_network_title_","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"In this study, the principle of competitive learning is used to develop an iterative algorithm for image recovery and segmentation. Within the framework of Markov Random Fields, the image recovery problem is transformed to the problem of minimization of an energy function. A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. Relationship of the update rule to Kohonen\u0026#39;s update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. Simulation experiments using this algorithm on real and synthetic images show promising results on smoothing within regions and also on enhancing the boundaries. Restoration results computer favorably with recently published results using Markov Random Fields and mean field approximation.","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"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":26870,"name":"Image segmentation","url":"https://www.academia.edu/Documents/in/Image_segmentation"},{"id":225304,"name":"Smoothing","url":"https://www.academia.edu/Documents/in/Smoothing"}],"urls":[{"id":41074163,"url":"https://doi.org/10.1117/12.162052"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="117458190"><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/117458190/Securing_dynamic_microgrid_partition_in_the_smart_grid"><img alt="Research paper thumbnail of Securing dynamic microgrid partition in the smart grid" class="work-thumbnail" src="https://attachments.academia-assets.com/113310143/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/117458190/Securing_dynamic_microgrid_partition_in_the_smart_grid">Securing dynamic microgrid partition in the smart grid</a></div><div class="wp-workCard_item"><span>International Journal of Distributed Sensor Networks</span><span>, May 1, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Message authentication has vital significance for dynamic microgrid partition in smart grid. Howe...</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">Message authentication has vital significance for dynamic microgrid partition in smart grid. However, current message authentication protocols based on ''public key infrastructure'' are too complicated to be deployed in smart grid and lack group information management function. On the other hand, group information management protocols based on ''logic key hierarchy'' need to broadcast a lot of messages during microgrid partition processes, resulting in high communication costs. To address these issues, we present a novel identity-based message authentication protocol for dynamic microgrid partition called securing dynamic microgrid partition. Similar to the protocols of this field, securing dynamic microgrid partition can provide message authentication and group information management functions. However, compared to other well-known approaches, securing dynamic microgrid partition uses Bloom filter for managing group information, which can reduce the communication cost of logic key hierarchy significantly. Moreover, securing dynamic microgrid partition uses Lagrange interpolation for designing new identity-based signing and verification algorithms, which is simple to be deployed in smart grid environment and much more efficient than current identity-based protocols. Experimental results show that the proposed approach is feasible for real-world applications.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e512d6eb221adbdb8c1d77461381d347" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113310143,"asset_id":117458190,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113310143/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="117458190"><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="117458190"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458190; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117458190]").text(description); $(".js-view-count[data-work-id=117458190]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117458190; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117458190']"); 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: "e512d6eb221adbdb8c1d77461381d347" } } $('.js-work-strip[data-work-id=117458190]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117458190,"title":"Securing dynamic microgrid partition in the smart grid","translated_title":"","metadata":{"publisher":"Hindawi Publishing Corporation","grobid_abstract":"Message authentication has vital significance for dynamic microgrid partition in smart grid. However, current message authentication protocols based on ''public key infrastructure'' are too complicated to be deployed in smart grid and lack group information management function. On the other hand, group information management protocols based on ''logic key hierarchy'' need to broadcast a lot of messages during microgrid partition processes, resulting in high communication costs. To address these issues, we present a novel identity-based message authentication protocol for dynamic microgrid partition called securing dynamic microgrid partition. Similar to the protocols of this field, securing dynamic microgrid partition can provide message authentication and group information management functions. However, compared to other well-known approaches, securing dynamic microgrid partition uses Bloom filter for managing group information, which can reduce the communication cost of logic key hierarchy significantly. Moreover, securing dynamic microgrid partition uses Lagrange interpolation for designing new identity-based signing and verification algorithms, which is simple to be deployed in smart grid environment and much more efficient than current identity-based protocols. Experimental results show that the proposed approach is feasible for real-world applications.","publication_date":{"day":1,"month":5,"year":2017,"errors":{}},"publication_name":"International Journal of Distributed Sensor Networks","grobid_abstract_attachment_id":113310143},"translated_abstract":null,"internal_url":"https://www.academia.edu/117458190/Securing_dynamic_microgrid_partition_in_the_smart_grid","translated_internal_url":"","created_at":"2024-04-13T20:44:35.306-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":113310143,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113310143/thumbnails/1.jpg","file_name":"1550147717711136.pdf","download_url":"https://www.academia.edu/attachments/113310143/download_file","bulk_download_file_name":"Securing_dynamic_microgrid_partition_in.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113310143/1550147717711136-libre.pdf?1713067225=\u0026response-content-disposition=attachment%3B+filename%3DSecuring_dynamic_microgrid_partition_in.pdf\u0026Expires=1742122266\u0026Signature=A7MiZTzJ1Z4Xfw7tjWTTKwQY7Jj8RO07Zye~9a5iiG7OzcYnhP~bIjVrEQj2BldTtKbUBkM4gT8U43vfoIlFU7Yn~5w5hlDrOIbGQ1G~ytH7ftNJH8SA8301cL5UKls00Ipnx765N-Mf~HHJtPq1fecVsDHQ7pMyOdzTySVvf4XzBvw7sikOIsjTOTxs5sgKZpTCbtxK2ZNlzlA9PL4esJyIr0bprL9r3DZP4sN8WMAviAOzN48q214LyuwXdlai7BO6Mh9bmRvXzAYq0Brvnv~2GnGID5jdjdKrQnKB9LyYYPiJCNxUY7d4RjrTzKnGOFaboOnNbT7IBXXTMhLnxw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Securing_dynamic_microgrid_partition_in_the_smart_grid","translated_slug":"","page_count":12,"language":"en","content_type":"Work","summary":"Message authentication has vital significance for dynamic microgrid partition in smart grid. However, current message authentication protocols based on ''public key infrastructure'' are too complicated to be deployed in smart grid and lack group information management function. On the other hand, group information management protocols based on ''logic key hierarchy'' need to broadcast a lot of messages during microgrid partition processes, resulting in high communication costs. To address these issues, we present a novel identity-based message authentication protocol for dynamic microgrid partition called securing dynamic microgrid partition. Similar to the protocols of this field, securing dynamic microgrid partition can provide message authentication and group information management functions. However, compared to other well-known approaches, securing dynamic microgrid partition uses Bloom filter for managing group information, which can reduce the communication cost of logic key hierarchy significantly. Moreover, securing dynamic microgrid partition uses Lagrange interpolation for designing new identity-based signing and verification algorithms, which is simple to be deployed in smart grid environment and much more efficient than current identity-based protocols. Experimental results show that the proposed approach is feasible for real-world applications.","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[{"id":113310143,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113310143/thumbnails/1.jpg","file_name":"1550147717711136.pdf","download_url":"https://www.academia.edu/attachments/113310143/download_file","bulk_download_file_name":"Securing_dynamic_microgrid_partition_in.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113310143/1550147717711136-libre.pdf?1713067225=\u0026response-content-disposition=attachment%3B+filename%3DSecuring_dynamic_microgrid_partition_in.pdf\u0026Expires=1742122266\u0026Signature=A7MiZTzJ1Z4Xfw7tjWTTKwQY7Jj8RO07Zye~9a5iiG7OzcYnhP~bIjVrEQj2BldTtKbUBkM4gT8U43vfoIlFU7Yn~5w5hlDrOIbGQ1G~ytH7ftNJH8SA8301cL5UKls00Ipnx765N-Mf~HHJtPq1fecVsDHQ7pMyOdzTySVvf4XzBvw7sikOIsjTOTxs5sgKZpTCbtxK2ZNlzlA9PL4esJyIr0bprL9r3DZP4sN8WMAviAOzN48q214LyuwXdlai7BO6Mh9bmRvXzAYq0Brvnv~2GnGID5jdjdKrQnKB9LyYYPiJCNxUY7d4RjrTzKnGOFaboOnNbT7IBXXTMhLnxw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":113310144,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113310144/thumbnails/1.jpg","file_name":"1550147717711136.pdf","download_url":"https://www.academia.edu/attachments/113310144/download_file","bulk_download_file_name":"Securing_dynamic_microgrid_partition_in.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113310144/1550147717711136-libre.pdf?1713067228=\u0026response-content-disposition=attachment%3B+filename%3DSecuring_dynamic_microgrid_partition_in.pdf\u0026Expires=1742122266\u0026Signature=YncZJcdGBoprsgaSbaS1iEsnCbKJD3CjlOd9fkMPXZTqWr~N7HdoKUvSM2c7Vpzr-ZJ3fyH-k6ZgaBpElXiI8S94zR0EDoJ9l2qLP1s0xxqNOm28XO7Au6J3Bv4Bs~nSKu-J1NUTj0LYKp63i4s6UbH9SF2-3~ULAkYXSpBsFSjBzzAdkqZZDtRTVAKcsPzUKWFK2bBlskBbR908Zd~NKwgfKWPQjrKQ4G5K2gTVi3Y1JKfpZhOBHRBh8rxvE8ywWd7yUHM3uZqv1RpXx0mdlTfs92qT5bB-lRDaTmmxMlj3uBEdbAWbEwlEcpmqOEb9rrU8pLBxrcHFzO5MgWg4aQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":422,"name":"Computer 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":26364,"name":"Smart Grid","url":"https://www.academia.edu/Documents/in/Smart_Grid"},{"id":38643,"name":"Microgrid","url":"https://www.academia.edu/Documents/in/Microgrid"},{"id":303727,"name":"Grid","url":"https://www.academia.edu/Documents/in/Grid"}],"urls":[{"id":41074162,"url":"https://journals.sagepub.com/doi/pdf/10.1177/1550147717711136"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="117458189"><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/117458189/Image_recovery_and_segmentation_using_competitive_learning_in_a_computational_network"><img alt="Research paper thumbnail of Image recovery and segmentation using competitive learning in a computational network" class="work-thumbnail" src="https://attachments.academia-assets.com/113310167/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/117458189/Image_recovery_and_segmentation_using_competitive_learning_in_a_computational_network">Image recovery and segmentation using competitive learning in a computational network</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In 1 his study, we have used the principle of competitive learning to develop an iterative algori...</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 1 his study, we have used the principle of competitive learning to develop an iterative algorithm for image recovery and segmentation. Within the framework of Markov random fields (MRF's), 1 he image recovery problem is transformed to the problem of minimization of an energy function. A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. The relationship of the update rule to Kohonen's update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. As compared to recently published results using mean field approximation, our algorithm shows consistently better performance in edge preservation and comparable performance in enhancing within the boundaries. 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If the monitoring of a dangerous environment is required, then one may not be able to deploy a wired network.</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="117458188"><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="117458188"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458188; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117458188]").text(description); $(".js-view-count[data-work-id=117458188]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117458188; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117458188']"); 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" } } $('.js-work-strip[data-work-id=117458188]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117458188,"title":"Coverage and Connectivity","translated_title":"","metadata":{"abstract":"A major advantage of wireless sensor networks (WSNs) over wired networks is the potential for ad hoc deployment of the network. 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The fact that the Government formulated or supplied the drawings, specifications, or other data does not license the holder or any other person or corporation; or convey any rights or permission to manufacture, use, or sell any patented invention that may relate to them. This report is the result of contracted fundamental research deemed exempt from public affairs security and policy review in accordance with SAF/AQR memorandum dated 10 Dec 08 and AFRL/CA policy clarification memorandum dated 16 Jan 09. This report is available to the general public, including foreign nationals. 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We present the design of an intelligent predictor that can be implemented on a Web server to guide caching strategies. Our approach is adaptive and learns the changing access patterns of pages in a Web site. The core of our predictor is a neural network that uses a back-propagation learning rule. We present results of the application of this predictor on static data using log files; it can be extended to learn the distribution of live Web page access patterns. Our simulations show fast learning, uniformly good prediction, and up to 82% correct prediction for the following six months based on a oneday training data. 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We present the design of an intelligent predictor that can be implemented on a Web server to guide caching strategies. Our approach is adaptive and learns the changing access patterns of pages in a Web site. The core of our predictor is a neural network that uses a back-propagation learning rule. We present results of the application of this predictor on static data using log files; it can be extended to learn the distribution of live Web page access patterns. Our simulations show fast learning, uniformly good prediction, and up to 82% correct prediction for the following six months based on a oneday training data. 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Since radio communication is the most energy-consuming operation, this framework aims to reduce the number of messages and the number of message collisions, while providing refined accuracy. The key element of the framework is a novel localization algorithm, called Ratiometric Vector Iteration (RVI). RVI is based on distance ratio estimates rather than absolute distance estimates which are often impossible to calculate. By iteratively updating the estimated location using the distance ratio, RVI localizes the target accurately with only three sensors' participation. After localization, the location of the target is reported to the subscriber. If the target is stationary or moves around within a small area, it is wasteful to report (almost) the same location estimates repeatedly. We, therefore, propose to dynamically adjust a reporting frequency considering the target's movement so that we can reduce the number of report messages while maintaining tracking quality. Extensive simulation results show that the proposed framework combining RVI and the movement-adaptive report scheduling algorithm reduces the localization error and total number of the transmitted messages up to half of those of the existing approaches.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="054483641df3f05be80fb99492c7f9d6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113310153,"asset_id":117458185,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113310153/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="117458185"><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="117458185"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458185; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117458185]").text(description); $(".js-view-count[data-work-id=117458185]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117458185; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117458185']"); 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: "054483641df3f05be80fb99492c7f9d6" } } $('.js-work-strip[data-work-id=117458185]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117458185,"title":"Wireless Sensor Networks","translated_title":"","metadata":{"ai_title_tag":"Energy-Efficient Target Localization in Wireless Sensor Networks","grobid_abstract":"In this paper, we propose and evaluate a distributed, energy-efficient, lightweight framework for target localization and tracking in wireless sensor networks. Since radio communication is the most energy-consuming operation, this framework aims to reduce the number of messages and the number of message collisions, while providing refined accuracy. The key element of the framework is a novel localization algorithm, called Ratiometric Vector Iteration (RVI). RVI is based on distance ratio estimates rather than absolute distance estimates which are often impossible to calculate. By iteratively updating the estimated location using the distance ratio, RVI localizes the target accurately with only three sensors' participation. After localization, the location of the target is reported to the subscriber. If the target is stationary or moves around within a small area, it is wasteful to report (almost) the same location estimates repeatedly. We, therefore, propose to dynamically adjust a reporting frequency considering the target's movement so that we can reduce the number of report messages while maintaining tracking quality. Extensive simulation results show that the proposed framework combining RVI and the movement-adaptive report scheduling algorithm reduces the localization error and total number of the transmitted messages up to half of those of the existing approaches.","publication_date":{"day":27,"month":9,"year":2010,"errors":{}},"grobid_abstract_attachment_id":113310153},"translated_abstract":null,"internal_url":"https://www.academia.edu/117458185/Wireless_Sensor_Networks","translated_internal_url":"","created_at":"2024-04-13T20:44:33.905-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":113310153,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113310153/thumbnails/1.jpg","file_name":"localization_comcom2006.pdf","download_url":"https://www.academia.edu/attachments/113310153/download_file","bulk_download_file_name":"Wireless_Sensor_Networks.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113310153/localization_comcom2006-libre.pdf?1713067218=\u0026response-content-disposition=attachment%3B+filename%3DWireless_Sensor_Networks.pdf\u0026Expires=1742122266\u0026Signature=c~VFV7U4QKmq7biDHIwtAbz3I0U1MabKvCRUkbf2m0NHDMOsKodCQ7jNaKtFTcTc9IwnatMNHzWnbNkleDoAEwtlnG6tufNX87AdGAxjY1Wm5ajTr79DXbVuRbggNYR5mAvXZKkpht6bhuIg4XgB274l5HGpOleXgySkpcCvqfkoGdtAJ7gTaX~v8o7JXT7wcg~R33jUzWR57~PZ1RRcZCKzaKyXfzEPe9ePUiN7wokDzEB1fr6n0U-qI2sHHo7H9V7Ic9DjhXel1SOcpKpm1quST~s894gpNTVEAXc1SWbXgYTDzYC2YJ4UMurLYy7vURkfq92FWiiuQlytyRQNRQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Wireless_Sensor_Networks","translated_slug":"","page_count":12,"language":"en","content_type":"Work","summary":"In this paper, we propose and evaluate a distributed, energy-efficient, lightweight framework for target localization and tracking in wireless sensor networks. Since radio communication is the most energy-consuming operation, this framework aims to reduce the number of messages and the number of message collisions, while providing refined accuracy. The key element of the framework is a novel localization algorithm, called Ratiometric Vector Iteration (RVI). RVI is based on distance ratio estimates rather than absolute distance estimates which are often impossible to calculate. By iteratively updating the estimated location using the distance ratio, RVI localizes the target accurately with only three sensors' participation. After localization, the location of the target is reported to the subscriber. If the target is stationary or moves around within a small area, it is wasteful to report (almost) the same location estimates repeatedly. We, therefore, propose to dynamically adjust a reporting frequency considering the target's movement so that we can reduce the number of report messages while maintaining tracking quality. Extensive simulation results show that the proposed framework combining RVI and the movement-adaptive report scheduling algorithm reduces the localization error and total number of the transmitted messages up to half of those of the existing approaches.","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[{"id":113310153,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113310153/thumbnails/1.jpg","file_name":"localization_comcom2006.pdf","download_url":"https://www.academia.edu/attachments/113310153/download_file","bulk_download_file_name":"Wireless_Sensor_Networks.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113310153/localization_comcom2006-libre.pdf?1713067218=\u0026response-content-disposition=attachment%3B+filename%3DWireless_Sensor_Networks.pdf\u0026Expires=1742122266\u0026Signature=c~VFV7U4QKmq7biDHIwtAbz3I0U1MabKvCRUkbf2m0NHDMOsKodCQ7jNaKtFTcTc9IwnatMNHzWnbNkleDoAEwtlnG6tufNX87AdGAxjY1Wm5ajTr79DXbVuRbggNYR5mAvXZKkpht6bhuIg4XgB274l5HGpOleXgySkpcCvqfkoGdtAJ7gTaX~v8o7JXT7wcg~R33jUzWR57~PZ1RRcZCKzaKyXfzEPe9ePUiN7wokDzEB1fr6n0U-qI2sHHo7H9V7Ic9DjhXel1SOcpKpm1quST~s894gpNTVEAXc1SWbXgYTDzYC2YJ4UMurLYy7vURkfq92FWiiuQlytyRQNRQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1211847,"name":"Wireless Sensor Network","url":"https://www.academia.edu/Documents/in/Wireless_Sensor_Network"}],"urls":[{"id":41074157,"url":"https://doi.org/10.1002/9780470890158.ch2"}]}, dispatcherData: dispatcherData }); 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It addresses the growing complexity and vocabulary in the field of Internet security, which has evolved alongside the rapid growth of the Internet and its vulnerabilities. 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For ...</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">The power-law distribution can be used to describe various aspects of social group behavior. For mussels, sociobiological research has shown that the Levy walk best describes their self-organizing movement strategy. A mussel&#39;s step length is drawn from a power-law distribution, and its direction is drawn from a uniform distribution. In the area of social networks, theories such as preferential attachment seek to explain why the degree distribution tends to be scale-free. The aim of this dissertation is to glean insight from these works to help solve problems in two domains: cloud computing systems and community detection. Privacy and security are two areas of concern for cloud systems. Recent research has provided evidence indicating how a malicious user could perform co-residence profiling and public to private IP mapping to target and exploit customers which share physical resources. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="117458176"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/117458176/Fuzzy_Evaluation_of_Agent_Based_Semantic_Match_Making_Algorithm_for_Cyberspace"><img alt="Research paper thumbnail of Fuzzy Evaluation of Agent-Based Semantic Match-Making Algorithm for Cyberspace" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/117458176/Fuzzy_Evaluation_of_Agent_Based_Semantic_Match_Making_Algorithm_for_Cyberspace">Fuzzy Evaluation of Agent-Based Semantic Match-Making Algorithm for Cyberspace</a></div><div class="wp-workCard_item"><span>Social Science Research Network</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="117458176"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="117458176"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458176; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112753182"><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/112753182/Continuous_Authentication_Using_One_class_Classifiers_and_their_Fusion"><img alt="Research paper thumbnail of Continuous Authentication Using One-class Classifiers and their Fusion" class="work-thumbnail" src="https://attachments.academia-assets.com/109886355/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/112753182/Continuous_Authentication_Using_One_class_Classifiers_and_their_Fusion">Continuous Authentication Using One-class Classifiers and their Fusion</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Oct 30, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">While developing continuous authentication systems (CAS), we generally assume that samples from b...</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">While developing continuous authentication systems (CAS), we generally assume that samples from both genuine and impostor classes are readily available. However, the assumption may not be true in certain circumstances. Therefore, we explore the possibility of implementing CAS using only genuine samples. Specifically, we investigate the usefulness of four one-class classifiers OCC (elliptic envelope, isolation forest, local outliers factor, and one-class support vector machines) and their fusion. The performance of these classifiers was evaluated on four distinct behavioral biometric datasets, and compared with eight multi-class classifiers (M CC). The results demonstrate that if we have sufficient training data from the genuine user the OCC, and their fusion can closely match the performance of the majority of M CC. Our findings encourage the research community to use OCC in order to build CAS as they do not require knowledge of impostor class during the enrollment process.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c53f080261eb7de0f2a05fa1f95e89d5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109886355,"asset_id":112753182,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109886355/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="112753182"><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="112753182"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112753182; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112753182]").text(description); $(".js-view-count[data-work-id=112753182]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112753182; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112753182']"); 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: "c53f080261eb7de0f2a05fa1f95e89d5" } } $('.js-work-strip[data-work-id=112753182]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112753182,"title":"Continuous Authentication Using One-class Classifiers and their Fusion","translated_title":"","metadata":{"publisher":"Cornell University","ai_title_tag":"One-Class Classifiers for Continuous Authentication","grobid_abstract":"While developing continuous authentication systems (CAS), we generally assume that samples from both genuine and impostor classes are readily available. However, the assumption may not be true in certain circumstances. Therefore, we explore the possibility of implementing CAS using only genuine samples. Specifically, we investigate the usefulness of four one-class classifiers OCC (elliptic envelope, isolation forest, local outliers factor, and one-class support vector machines) and their fusion. The performance of these classifiers was evaluated on four distinct behavioral biometric datasets, and compared with eight multi-class classifiers (M CC). The results demonstrate that if we have sufficient training data from the genuine user the OCC, and their fusion can closely match the performance of the majority of M CC. Our findings encourage the research community to use OCC in order to build CAS as they do not require knowledge of impostor class during the enrollment process.","publication_date":{"day":30,"month":10,"year":2017,"errors":{}},"publication_name":"arXiv (Cornell University)","grobid_abstract_attachment_id":109886355},"translated_abstract":null,"internal_url":"https://www.academia.edu/112753182/Continuous_Authentication_Using_One_class_Classifiers_and_their_Fusion","translated_internal_url":"","created_at":"2024-01-01T19:02:56.339-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":109886355,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109886355/thumbnails/1.jpg","file_name":"1710.pdf","download_url":"https://www.academia.edu/attachments/109886355/download_file","bulk_download_file_name":"Continuous_Authentication_Using_One_clas.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109886355/1710-libre.pdf?1704164632=\u0026response-content-disposition=attachment%3B+filename%3DContinuous_Authentication_Using_One_clas.pdf\u0026Expires=1742122266\u0026Signature=TkPdHnfpLZBIe6RlRwfqrutRRE3GZ9Hpp1FBqUrYBamq0dFOf774knghKD6UTbFjYIt3ifKbEZkqvQFXao1M4KD4hBakt4Q2yxHyj0W68urVlaFc~L1k3vNvykGAhJTtJ02LN9x7wAPUjaA2Neg03IXtZ1MWDtRT501Zf6SMOIbTKctRfZNJrh4eYUZMgj2QKm83AE6EkHN~Nq~ZXx6oxLQZyHDvu3w2Gi36xp6DFQHtVUqbDVRlkoKmNAmvPJGv17Vr~-jHe9zZHkdtomPS2~v55AeKySKeMKpZGSnPL1vnINJR98U4vcGvacc8Fj2YyMicUbbOBLkMArRWlJ0Q5g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Continuous_Authentication_Using_One_class_Classifiers_and_their_Fusion","translated_slug":"","page_count":8,"language":"en","content_type":"Work","summary":"While developing continuous authentication systems (CAS), we generally assume that samples from both genuine and impostor classes are readily available. However, the assumption may not be true in certain circumstances. Therefore, we explore the possibility of implementing CAS using only genuine samples. Specifically, we investigate the usefulness of four one-class classifiers OCC (elliptic envelope, isolation forest, local outliers factor, and one-class support vector machines) and their fusion. The performance of these classifiers was evaluated on four distinct behavioral biometric datasets, and compared with eight multi-class classifiers (M CC). The results demonstrate that if we have sufficient training data from the genuine user the OCC, and their fusion can closely match the performance of the majority of M CC. Our findings encourage the research community to use OCC in order to build CAS as they do not require knowledge of impostor class during the enrollment process.","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[{"id":109886355,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109886355/thumbnails/1.jpg","file_name":"1710.pdf","download_url":"https://www.academia.edu/attachments/109886355/download_file","bulk_download_file_name":"Continuous_Authentication_Using_One_clas.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109886355/1710-libre.pdf?1704164632=\u0026response-content-disposition=attachment%3B+filename%3DContinuous_Authentication_Using_One_clas.pdf\u0026Expires=1742122266\u0026Signature=TkPdHnfpLZBIe6RlRwfqrutRRE3GZ9Hpp1FBqUrYBamq0dFOf774knghKD6UTbFjYIt3ifKbEZkqvQFXao1M4KD4hBakt4Q2yxHyj0W68urVlaFc~L1k3vNvykGAhJTtJ02LN9x7wAPUjaA2Neg03IXtZ1MWDtRT501Zf6SMOIbTKctRfZNJrh4eYUZMgj2QKm83AE6EkHN~Nq~ZXx6oxLQZyHDvu3w2Gi36xp6DFQHtVUqbDVRlkoKmNAmvPJGv17Vr~-jHe9zZHkdtomPS2~v55AeKySKeMKpZGSnPL1vnINJR98U4vcGvacc8Fj2YyMicUbbOBLkMArRWlJ0Q5g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"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":9173,"name":"Biometrics","url":"https://www.academia.edu/Documents/in/Biometrics"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine"},{"id":1941885,"name":"Outlier","url":"https://www.academia.edu/Documents/in/Outlier"}],"urls":[{"id":38060607,"url":"http://arxiv.org/pdf/1710.11075"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112753181"><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/112753181/A_Non_interactive_Dual_channel_Authentication_Protocol_for_Assuring_Pseudo_confidentiality"><img alt="Research paper thumbnail of A Non-interactive Dual-channel Authentication Protocol for Assuring Pseudo-confidentiality" 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"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/112753181/A_Non_interactive_Dual_channel_Authentication_Protocol_for_Assuring_Pseudo_confidentiality">A Non-interactive Dual-channel Authentication Protocol for Assuring Pseudo-confidentiality</a></div><div class="wp-workCard_item"><span>Network and Distributed System Security Symposium</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We introduce a non-interactive dual channel authentication protocol and apply it to long distance...</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">We introduce a non-interactive dual channel authentication protocol and apply it to long distance communication for assuring pseudo-confidentiality, a criteria that prevents a malicious agent from exfiltrating information to unauthorized destinations. Unlike previously proposed protocols that assume a manual (human-aided) or equivalent authenticated channel, our protocol utilizes a non-manual authenticated channel. We analyze the security properties for a possible realization of this protocol and develop a prototype. Through a Raspberry-Pi implementation, we show how the incorporation of the proposed scheme into the future design of keyboard interfaces may impact authentication practices. 1. Non-interactive dual channel authentication protocols Non-interactive dual channel authentication protocols employ two channels and authenticate information r1 received through one presumably insecure channel using a piece of brief information r2 (computed from r1) received through the other presumably authenticated channel. To remain lightweight these protocols employ one way communication from a message sender, Alice, to a message recipient, Bob. It can be shown that in these protocols, imposters cannot authenticate themselves if, (i) only one of the channels is compromised or (ii) both channels are compromised but attacks on them are not coordinated. Existing literature describes a family of non-interactive message authentication protocols (e.g., [1, 2, 4, 3, 5]), known as NIMAPs, that use a manual (human-aided) authenticated channel. In these protocols, a hash value for each message being sent is generated. Alice then transmits This work is supported by AFOSR Grants FA9550-09-1-0479 and FA 9550-09-1-0165. the message and hash over the insecure and authenticated channels respectively to Bob. In some protocols, a key is applied to the hash function when generating the hash value and this key is sent with the message over the insecure channel [3, 5] or with the hash value over the authenticated channel [2]. NIMAPs assume that the authenticated channel is human-aided hence adversarial influence on this channel is significantly limited. This assumption is sufficient for short distance communications requiring infrequent authentication of messages. However, this limits their application in long distance communications (e.g, over the internet) where frequent authentication is required and a human-aided authenticated channel is unrealistic and/or infeasible. 2. Proposed protocol Replacement of the human-aided authenticated channel in NIMAPs erodes the security benefit assured by such a channel. This exposes them (NIMAPs) to channel spoof attacks where an adversary, Eve, could spoof the identity of the authenticated channel when sending her authentication messages to Bob. Because of the non-interactive nature of Figure 1. Proposed protocol (Generic) the protocol, Bob has no way to know whether these authentications are sent by Alice hence, he will accept a message if its authentication is valid (i.e., it authenticates the message sent through the insecure channel). We address this issue by introducing the following: (i) we assign the task of Alice to a small hardware attachment that we assume not to be affected by a channel compromise, and (ii) we assume that it is difficult for the adversary, Eve to compute r2 given r1. Figure 1 presents the proposed generic protocol. In this protocol, Alice is a hardware attachment to the keyboard with the following properties: (i) She has processing capabilities to parse typed keystrokes and apply a method generate to identify r1 and to compute r2 from r1; (ii) She only receives input from the keyboard; (iii) She sends output to Bob using two different channels (one is insecure that runs through the host and the other is authenticated that bypasses the host). Bob is a verifier program located in a remote computer. In Figure 1 (and in subsequent protocol figures), the “′” added to information received by Bob indicates that the sent and received values might be different and the insecure and authenticated channels are represented by “→” and “⇒” respectively. The protocol is designed based on the following assumptions: (i) an r2 can verify the associated r1; (ii) an adversary can generate an r1 or snoop the r1 generated by Alice but it is difficult for her to compute the associated r2 from a given r1; (iii) Bob can compute r2 from r1; (iv) r′ 1 is accepted only if (r′ 1, r′ 2) is consistent i.e., r2b (the expected r′ 2 computed by Bob for the r′ 1 he received) is the same as r′ 2. Alice generates r1 and r2 from the keystrokes typed by a user and sends r1 through the possibly compromised host using the insecure channel and r2 directly to Bob using the authenticated channel. When Bob receives r1 and r2 from Alice, he accepts r′ 1 only if (r′ 1, r′ 2) is consistent. To defeat this protocol, Eve either; (i) waits for Alice to transmit an r1 and replaces r1 with r1eve and expects that it will be…</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="112753181"><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="112753181"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112753181; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112753181]").text(description); $(".js-view-count[data-work-id=112753181]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112753181; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112753181']"); 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" } } $('.js-work-strip[data-work-id=112753181]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112753181,"title":"A Non-interactive Dual-channel Authentication Protocol for Assuring Pseudo-confidentiality","translated_title":"","metadata":{"abstract":"We introduce a non-interactive dual channel authentication protocol and apply it to long distance communication for assuring pseudo-confidentiality, a criteria that prevents a malicious agent from exfiltrating information to unauthorized destinations. Unlike previously proposed protocols that assume a manual (human-aided) or equivalent authenticated channel, our protocol utilizes a non-manual authenticated channel. We analyze the security properties for a possible realization of this protocol and develop a prototype. Through a Raspberry-Pi implementation, we show how the incorporation of the proposed scheme into the future design of keyboard interfaces may impact authentication practices. 1. Non-interactive dual channel authentication protocols Non-interactive dual channel authentication protocols employ two channels and authenticate information r1 received through one presumably insecure channel using a piece of brief information r2 (computed from r1) received through the other presumably authenticated channel. To remain lightweight these protocols employ one way communication from a message sender, Alice, to a message recipient, Bob. It can be shown that in these protocols, imposters cannot authenticate themselves if, (i) only one of the channels is compromised or (ii) both channels are compromised but attacks on them are not coordinated. Existing literature describes a family of non-interactive message authentication protocols (e.g., [1, 2, 4, 3, 5]), known as NIMAPs, that use a manual (human-aided) authenticated channel. In these protocols, a hash value for each message being sent is generated. Alice then transmits This work is supported by AFOSR Grants FA9550-09-1-0479 and FA 9550-09-1-0165. the message and hash over the insecure and authenticated channels respectively to Bob. In some protocols, a key is applied to the hash function when generating the hash value and this key is sent with the message over the insecure channel [3, 5] or with the hash value over the authenticated channel [2]. NIMAPs assume that the authenticated channel is human-aided hence adversarial influence on this channel is significantly limited. This assumption is sufficient for short distance communications requiring infrequent authentication of messages. However, this limits their application in long distance communications (e.g, over the internet) where frequent authentication is required and a human-aided authenticated channel is unrealistic and/or infeasible. 2. Proposed protocol Replacement of the human-aided authenticated channel in NIMAPs erodes the security benefit assured by such a channel. This exposes them (NIMAPs) to channel spoof attacks where an adversary, Eve, could spoof the identity of the authenticated channel when sending her authentication messages to Bob. Because of the non-interactive nature of Figure 1. Proposed protocol (Generic) the protocol, Bob has no way to know whether these authentications are sent by Alice hence, he will accept a message if its authentication is valid (i.e., it authenticates the message sent through the insecure channel). We address this issue by introducing the following: (i) we assign the task of Alice to a small hardware attachment that we assume not to be affected by a channel compromise, and (ii) we assume that it is difficult for the adversary, Eve to compute r2 given r1. Figure 1 presents the proposed generic protocol. In this protocol, Alice is a hardware attachment to the keyboard with the following properties: (i) She has processing capabilities to parse typed keystrokes and apply a method generate to identify r1 and to compute r2 from r1; (ii) She only receives input from the keyboard; (iii) She sends output to Bob using two different channels (one is insecure that runs through the host and the other is authenticated that bypasses the host). Bob is a verifier program located in a remote computer. In Figure 1 (and in subsequent protocol figures), the “′” added to information received by Bob indicates that the sent and received values might be different and the insecure and authenticated channels are represented by “→” and “⇒” respectively. The protocol is designed based on the following assumptions: (i) an r2 can verify the associated r1; (ii) an adversary can generate an r1 or snoop the r1 generated by Alice but it is difficult for her to compute the associated r2 from a given r1; (iii) Bob can compute r2 from r1; (iv) r′ 1 is accepted only if (r′ 1, r′ 2) is consistent i.e., r2b (the expected r′ 2 computed by Bob for the r′ 1 he received) is the same as r′ 2. Alice generates r1 and r2 from the keystrokes typed by a user and sends r1 through the possibly compromised host using the insecure channel and r2 directly to Bob using the authenticated channel. When Bob receives r1 and r2 from Alice, he accepts r′ 1 only if (r′ 1, r′ 2) is consistent. To defeat this protocol, Eve either; (i) waits for Alice to transmit an r1 and replaces r1 with r1eve and expects that it will be…","publication_date":{"day":null,"month":null,"year":2013,"errors":{}},"publication_name":"Network and Distributed System Security Symposium"},"translated_abstract":"We introduce a non-interactive dual channel authentication protocol and apply it to long distance communication for assuring pseudo-confidentiality, a criteria that prevents a malicious agent from exfiltrating information to unauthorized destinations. Unlike previously proposed protocols that assume a manual (human-aided) or equivalent authenticated channel, our protocol utilizes a non-manual authenticated channel. We analyze the security properties for a possible realization of this protocol and develop a prototype. Through a Raspberry-Pi implementation, we show how the incorporation of the proposed scheme into the future design of keyboard interfaces may impact authentication practices. 1. Non-interactive dual channel authentication protocols Non-interactive dual channel authentication protocols employ two channels and authenticate information r1 received through one presumably insecure channel using a piece of brief information r2 (computed from r1) received through the other presumably authenticated channel. To remain lightweight these protocols employ one way communication from a message sender, Alice, to a message recipient, Bob. It can be shown that in these protocols, imposters cannot authenticate themselves if, (i) only one of the channels is compromised or (ii) both channels are compromised but attacks on them are not coordinated. Existing literature describes a family of non-interactive message authentication protocols (e.g., [1, 2, 4, 3, 5]), known as NIMAPs, that use a manual (human-aided) authenticated channel. In these protocols, a hash value for each message being sent is generated. Alice then transmits This work is supported by AFOSR Grants FA9550-09-1-0479 and FA 9550-09-1-0165. the message and hash over the insecure and authenticated channels respectively to Bob. In some protocols, a key is applied to the hash function when generating the hash value and this key is sent with the message over the insecure channel [3, 5] or with the hash value over the authenticated channel [2]. NIMAPs assume that the authenticated channel is human-aided hence adversarial influence on this channel is significantly limited. This assumption is sufficient for short distance communications requiring infrequent authentication of messages. However, this limits their application in long distance communications (e.g, over the internet) where frequent authentication is required and a human-aided authenticated channel is unrealistic and/or infeasible. 2. Proposed protocol Replacement of the human-aided authenticated channel in NIMAPs erodes the security benefit assured by such a channel. This exposes them (NIMAPs) to channel spoof attacks where an adversary, Eve, could spoof the identity of the authenticated channel when sending her authentication messages to Bob. Because of the non-interactive nature of Figure 1. Proposed protocol (Generic) the protocol, Bob has no way to know whether these authentications are sent by Alice hence, he will accept a message if its authentication is valid (i.e., it authenticates the message sent through the insecure channel). We address this issue by introducing the following: (i) we assign the task of Alice to a small hardware attachment that we assume not to be affected by a channel compromise, and (ii) we assume that it is difficult for the adversary, Eve to compute r2 given r1. Figure 1 presents the proposed generic protocol. In this protocol, Alice is a hardware attachment to the keyboard with the following properties: (i) She has processing capabilities to parse typed keystrokes and apply a method generate to identify r1 and to compute r2 from r1; (ii) She only receives input from the keyboard; (iii) She sends output to Bob using two different channels (one is insecure that runs through the host and the other is authenticated that bypasses the host). Bob is a verifier program located in a remote computer. In Figure 1 (and in subsequent protocol figures), the “′” added to information received by Bob indicates that the sent and received values might be different and the insecure and authenticated channels are represented by “→” and “⇒” respectively. The protocol is designed based on the following assumptions: (i) an r2 can verify the associated r1; (ii) an adversary can generate an r1 or snoop the r1 generated by Alice but it is difficult for her to compute the associated r2 from a given r1; (iii) Bob can compute r2 from r1; (iv) r′ 1 is accepted only if (r′ 1, r′ 2) is consistent i.e., r2b (the expected r′ 2 computed by Bob for the r′ 1 he received) is the same as r′ 2. Alice generates r1 and r2 from the keystrokes typed by a user and sends r1 through the possibly compromised host using the insecure channel and r2 directly to Bob using the authenticated channel. When Bob receives r1 and r2 from Alice, he accepts r′ 1 only if (r′ 1, r′ 2) is consistent. To defeat this protocol, Eve either; (i) waits for Alice to transmit an r1 and replaces r1 with r1eve and expects that it will be…","internal_url":"https://www.academia.edu/112753181/A_Non_interactive_Dual_channel_Authentication_Protocol_for_Assuring_Pseudo_confidentiality","translated_internal_url":"","created_at":"2024-01-01T19:02:56.156-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_Non_interactive_Dual_channel_Authentication_Protocol_for_Assuring_Pseudo_confidentiality","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"We introduce a non-interactive dual channel authentication protocol and apply it to long distance communication for assuring pseudo-confidentiality, a criteria that prevents a malicious agent from exfiltrating information to unauthorized destinations. Unlike previously proposed protocols that assume a manual (human-aided) or equivalent authenticated channel, our protocol utilizes a non-manual authenticated channel. We analyze the security properties for a possible realization of this protocol and develop a prototype. Through a Raspberry-Pi implementation, we show how the incorporation of the proposed scheme into the future design of keyboard interfaces may impact authentication practices. 1. Non-interactive dual channel authentication protocols Non-interactive dual channel authentication protocols employ two channels and authenticate information r1 received through one presumably insecure channel using a piece of brief information r2 (computed from r1) received through the other presumably authenticated channel. To remain lightweight these protocols employ one way communication from a message sender, Alice, to a message recipient, Bob. It can be shown that in these protocols, imposters cannot authenticate themselves if, (i) only one of the channels is compromised or (ii) both channels are compromised but attacks on them are not coordinated. Existing literature describes a family of non-interactive message authentication protocols (e.g., [1, 2, 4, 3, 5]), known as NIMAPs, that use a manual (human-aided) authenticated channel. In these protocols, a hash value for each message being sent is generated. Alice then transmits This work is supported by AFOSR Grants FA9550-09-1-0479 and FA 9550-09-1-0165. the message and hash over the insecure and authenticated channels respectively to Bob. In some protocols, a key is applied to the hash function when generating the hash value and this key is sent with the message over the insecure channel [3, 5] or with the hash value over the authenticated channel [2]. NIMAPs assume that the authenticated channel is human-aided hence adversarial influence on this channel is significantly limited. This assumption is sufficient for short distance communications requiring infrequent authentication of messages. However, this limits their application in long distance communications (e.g, over the internet) where frequent authentication is required and a human-aided authenticated channel is unrealistic and/or infeasible. 2. Proposed protocol Replacement of the human-aided authenticated channel in NIMAPs erodes the security benefit assured by such a channel. This exposes them (NIMAPs) to channel spoof attacks where an adversary, Eve, could spoof the identity of the authenticated channel when sending her authentication messages to Bob. Because of the non-interactive nature of Figure 1. Proposed protocol (Generic) the protocol, Bob has no way to know whether these authentications are sent by Alice hence, he will accept a message if its authentication is valid (i.e., it authenticates the message sent through the insecure channel). We address this issue by introducing the following: (i) we assign the task of Alice to a small hardware attachment that we assume not to be affected by a channel compromise, and (ii) we assume that it is difficult for the adversary, Eve to compute r2 given r1. Figure 1 presents the proposed generic protocol. In this protocol, Alice is a hardware attachment to the keyboard with the following properties: (i) She has processing capabilities to parse typed keystrokes and apply a method generate to identify r1 and to compute r2 from r1; (ii) She only receives input from the keyboard; (iii) She sends output to Bob using two different channels (one is insecure that runs through the host and the other is authenticated that bypasses the host). Bob is a verifier program located in a remote computer. In Figure 1 (and in subsequent protocol figures), the “′” added to information received by Bob indicates that the sent and received values might be different and the insecure and authenticated channels are represented by “→” and “⇒” respectively. The protocol is designed based on the following assumptions: (i) an r2 can verify the associated r1; (ii) an adversary can generate an r1 or snoop the r1 generated by Alice but it is difficult for her to compute the associated r2 from a given r1; (iii) Bob can compute r2 from r1; (iv) r′ 1 is accepted only if (r′ 1, r′ 2) is consistent i.e., r2b (the expected r′ 2 computed by Bob for the r′ 1 he received) is the same as r′ 2. Alice generates r1 and r2 from the keystrokes typed by a user and sends r1 through the possibly compromised host using the insecure channel and r2 directly to Bob using the authenticated channel. When Bob receives r1 and r2 from Alice, he accepts r′ 1 only if (r′ 1, r′ 2) is consistent. To defeat this protocol, Eve either; (i) waits for Alice to transmit an r1 and replaces r1 with r1eve and expects that it will be…","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":13923,"name":"Computer Security","url":"https://www.academia.edu/Documents/in/Computer_Security"},{"id":62540,"name":"Computer Network","url":"https://www.academia.edu/Documents/in/Computer_Network"},{"id":196193,"name":"Confidentiality","url":"https://www.academia.edu/Documents/in/Confidentiality"},{"id":2176821,"name":"Authentication Protocol","url":"https://www.academia.edu/Documents/in/Authentication_Protocol"},{"id":3273604,"name":"Dual (grammatical number)","url":"https://www.academia.edu/Documents/in/Dual_grammatical_number_"}],"urls":[{"id":38060606,"url":"https://dblp.uni-trier.de/db/conf/ndss/ndss2013.html#IrakizaKP13"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112753180"><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/112753180/Looking_Through_Your_Smartphone_Screen_to_Steal_Your_Pin_Using_a_3D_Camera"><img alt="Research paper thumbnail of Looking Through Your Smartphone Screen to Steal Your Pin Using a 3D Camera" 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"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/112753180/Looking_Through_Your_Smartphone_Screen_to_Steal_Your_Pin_Using_a_3D_Camera">Looking Through Your Smartphone Screen to Steal Your Pin Using a 3D Camera</a></div><div class="wp-workCard_item"><span>Advances in intelligent systems and computing</span><span>, Nov 2, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Recent research shows that video recordings of the user’s hand movement and his or her smartphone...</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">Recent research shows that video recordings of the user’s hand movement and his or her smartphone screen display can be used to steal sensitive information such as pins and passwords. The methods presented in the past assume the victim to be present in a well illuminated place. In this paper, we present a novel attack on the smartphone users’ pins that does not require a highly illuminated room and works even in the complete darkness. We use a DS325 Soft Kinect camera to record the users’ interaction with their smartphones while they type their pins. Using the 900 short RGBD video recording of the pin entry process from 30 different users, we show our attack was able to break 43% of the pins in the first attempt and 61% of the pins in the first 10 attempts. With the advancements in the quality and accessibility of the depth-sensing cameras day by day, we believe our work exposes a major security risk in the present and future and calls the community to take a closer look at the security measures for the usage of the smart devices.</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="112753180"><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="112753180"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112753180; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112753180]").text(description); $(".js-view-count[data-work-id=112753180]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112753180; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112753180']"); 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" } } $('.js-work-strip[data-work-id=112753180]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112753180,"title":"Looking Through Your Smartphone Screen to Steal Your Pin Using a 3D Camera","translated_title":"","metadata":{"abstract":"Recent research shows that video recordings of the user’s hand movement and his or her smartphone screen display can be used to steal sensitive information such as pins and passwords. The methods presented in the past assume the victim to be present in a well illuminated place. In this paper, we present a novel attack on the smartphone users’ pins that does not require a highly illuminated room and works even in the complete darkness. We use a DS325 Soft Kinect camera to record the users’ interaction with their smartphones while they type their pins. Using the 900 short RGBD video recording of the pin entry process from 30 different users, we show our attack was able to break 43% of the pins in the first attempt and 61% of the pins in the first 10 attempts. With the advancements in the quality and accessibility of the depth-sensing cameras day by day, we believe our work exposes a major security risk in the present and future and calls the community to take a closer look at the security measures for the usage of the smart devices.","publisher":"Springer Nature","publication_date":{"day":2,"month":11,"year":2018,"errors":{}},"publication_name":"Advances in intelligent systems and computing"},"translated_abstract":"Recent research shows that video recordings of the user’s hand movement and his or her smartphone screen display can be used to steal sensitive information such as pins and passwords. The methods presented in the past assume the victim to be present in a well illuminated place. In this paper, we present a novel attack on the smartphone users’ pins that does not require a highly illuminated room and works even in the complete darkness. We use a DS325 Soft Kinect camera to record the users’ interaction with their smartphones while they type their pins. Using the 900 short RGBD video recording of the pin entry process from 30 different users, we show our attack was able to break 43% of the pins in the first attempt and 61% of the pins in the first 10 attempts. With the advancements in the quality and accessibility of the depth-sensing cameras day by day, we believe our work exposes a major security risk in the present and future and calls the community to take a closer look at the security measures for the usage of the smart devices.","internal_url":"https://www.academia.edu/112753180/Looking_Through_Your_Smartphone_Screen_to_Steal_Your_Pin_Using_a_3D_Camera","translated_internal_url":"","created_at":"2024-01-01T19:02:55.947-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Looking_Through_Your_Smartphone_Screen_to_Steal_Your_Pin_Using_a_3D_Camera","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Recent research shows that video recordings of the user’s hand movement and his or her smartphone screen display can be used to steal sensitive information such as pins and passwords. The methods presented in the past assume the victim to be present in a well illuminated place. In this paper, we present a novel attack on the smartphone users’ pins that does not require a highly illuminated room and works even in the complete darkness. We use a DS325 Soft Kinect camera to record the users’ interaction with their smartphones while they type their pins. Using the 900 short RGBD video recording of the pin entry process from 30 different users, we show our attack was able to break 43% of the pins in the first attempt and 61% of the pins in the first 10 attempts. With the advancements in the quality and accessibility of the depth-sensing cameras day by day, we believe our work exposes a major security risk in the present and future and calls the community to take a closer look at the security measures for the usage of the smart devices.","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":13923,"name":"Computer Security","url":"https://www.academia.edu/Documents/in/Computer_Security"},{"id":433921,"name":"Password","url":"https://www.academia.edu/Documents/in/Password"}],"urls":[{"id":38060605,"url":"https://doi.org/10.1007/978-3-030-01177-2_73"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112753179"><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/112753179/Security_in_WSNs"><img alt="Research paper thumbnail of Security in WSNs" 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"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/112753179/Security_in_WSNs">Security in WSNs</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Security in wireless sensor networks (WSNs) is centered on six fundamental requirements namely, a...</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">Security in wireless sensor networks (WSNs) is centered on six fundamental requirements namely, authentication, confidentiality, integrity, reliability, availability and data freshness [4, 39, 52, 62]. In this chapter, we describe these requirements, the different kinds of attacks that aim to compromise these requirements (and hence the security of a WSN) and the defense mechanisms that can be employed to overcome these attacks.</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="112753179"><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="112753179"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112753179; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112753179]").text(description); $(".js-view-count[data-work-id=112753179]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112753179; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112753179']"); 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" } } $('.js-work-strip[data-work-id=112753179]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112753179,"title":"Security in WSNs","translated_title":"","metadata":{"abstract":"Security in wireless sensor networks (WSNs) is centered on six fundamental requirements namely, authentication, confidentiality, integrity, reliability, availability and data freshness [4, 39, 52, 62]. In this chapter, we describe these requirements, the different kinds of attacks that aim to compromise these requirements (and hence the security of a WSN) and the defense mechanisms that can be employed to overcome these attacks.","publication_date":{"day":null,"month":null,"year":2016,"errors":{}}},"translated_abstract":"Security in wireless sensor networks (WSNs) is centered on six fundamental requirements namely, authentication, confidentiality, integrity, reliability, availability and data freshness [4, 39, 52, 62]. In this chapter, we describe these requirements, the different kinds of attacks that aim to compromise these requirements (and hence the security of a WSN) and the defense mechanisms that can be employed to overcome these attacks.","internal_url":"https://www.academia.edu/112753179/Security_in_WSNs","translated_internal_url":"","created_at":"2024-01-01T19:02:55.751-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Security_in_WSNs","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Security in wireless sensor networks (WSNs) is centered on six fundamental requirements namely, authentication, confidentiality, integrity, reliability, availability and data freshness [4, 39, 52, 62]. In this chapter, we describe these requirements, the different kinds of attacks that aim to compromise these requirements (and hence the security of a WSN) and the defense mechanisms that can be employed to overcome these attacks.","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":9136,"name":"Wireless Sensor Networks","url":"https://www.academia.edu/Documents/in/Wireless_Sensor_Networks"},{"id":13923,"name":"Computer Security","url":"https://www.academia.edu/Documents/in/Computer_Security"},{"id":116557,"name":"Compromise","url":"https://www.academia.edu/Documents/in/Compromise"},{"id":196193,"name":"Confidentiality","url":"https://www.academia.edu/Documents/in/Confidentiality"},{"id":341805,"name":"Data Integrity","url":"https://www.academia.edu/Documents/in/Data_Integrity"},{"id":1211847,"name":"Wireless Sensor Network","url":"https://www.academia.edu/Documents/in/Wireless_Sensor_Network"}],"urls":[{"id":38060604,"url":"https://doi.org/10.1007/978-3-319-46769-6_4"}]}, dispatcherData: dispatcherData }); 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112753177]").text(description); $(".js-view-count[data-work-id=112753177]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112753177; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112753177']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112753176"><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/112753176/Fake_News_Early_Detection_A_Theory_driven_Model"><img alt="Research paper thumbnail of Fake News Early Detection: A Theory-driven Model" class="work-thumbnail" src="https://attachments.academia-assets.com/109886356/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/112753176/Fake_News_Early_Detection_A_Theory_driven_Model">Fake News Early Detection: A Theory-driven Model</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Apr 26, 2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3a94f413bd8b5ef35679791842ee0ace" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109886356,"asset_id":112753176,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109886356/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="112753176"><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="112753176"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112753176; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112753175"><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/112753175/Wireless_Sensor_Networks_Security_Coverage_and_Localization"><img alt="Research paper thumbnail of Wireless Sensor Networks: Security, Coverage, and Localization" 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"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/112753175/Wireless_Sensor_Networks_Security_Coverage_and_Localization">Wireless Sensor Networks: Security, Coverage, and Localization</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This book presents a comprehensive overview of wireless sensor networks (WSNs) with an emphasis o...</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 book presents a comprehensive overview of wireless sensor networks (WSNs) with an emphasis on security, coverage, and localization. It offers a structural treatment of WSN building blocks including hardware and protocol architectures and also provides a systems-level view of how WSNs operate. These building blocks will allow readers to program specialized applications and conduct research in advanced topics. A brief introductory chapter covers common applications and communication protocols for WSNs. Next, the authors review basic mathematical models such as Voroni diagrams and Delaunay triangulations. Sensor principles, hardware structure, and medium access protocols are examined. Security challenges ranging from defense strategies to network robustness are explored, along with quality of service measures. Finally, this book discusses recent developments and future directions in WSN platforms. Each chapter concludes with classroom-tested exercises that reinforce key concepts. This book is suitable for researchers and for practitioners in industry. 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Microgrids can deliver power efficiently, cost-effectively and environment-friendly. To design a sustainable microgrid; several tasks such as resource allocation, optimization, power flow analyses, load demand analysis, cost analysis, etc., needs to be performed for a sustainable operation. The system should be able to process periodically a plethora of data originating from various sensors and measurement units. The system needs to process the data and then deliver appropriate control signals when some changes in the system occurs. Cloud computing can be an efficient way to handle this huge data processing task to meet various microgrid operational needs. It will also eliminate the need for a local macro server and thus will reduce the overall cost of the microgrid operation.</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="117458193"><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="117458193"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458193; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117458193]").text(description); $(".js-view-count[data-work-id=117458193]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117458193; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117458193']"); 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" } } $('.js-work-strip[data-work-id=117458193]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117458193,"title":"Resource Management for Uninterrupted Microgrid Operation","translated_title":"","metadata":{"abstract":"To meet the ever-increasing demand of electric power, microgrids are establishing themselves to be one of the most reliable power delivery systems. 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Cloud computing can be an efficient way to handle this huge data processing task to meet various microgrid operational needs. It will also eliminate the need for a local macro server and thus will reduce the overall cost of the microgrid operation.","internal_url":"https://www.academia.edu/117458193/Resource_Management_for_Uninterrupted_Microgrid_Operation","translated_internal_url":"","created_at":"2024-04-13T20:44:36.714-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Resource_Management_for_Uninterrupted_Microgrid_Operation","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"To meet the ever-increasing demand of electric power, microgrids are establishing themselves to be one of the most reliable power delivery systems. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="117458191"><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/117458191/_title_Image_recovery_and_segmentation_using_competitive_learning_in_a_layered_network_title_"><img alt="Research paper thumbnail of <title>Image recovery and segmentation using competitive learning in a layered network</title>" 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"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/117458191/_title_Image_recovery_and_segmentation_using_competitive_learning_in_a_layered_network_title_"><title>Image recovery and segmentation using competitive learning in a layered network</title></a></div><div class="wp-workCard_item"><span>Proceedings of SPIE</span><span>, Oct 29, 1993</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this study, the principle of competitive learning is used to develop an iterative algorithm fo...</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 this study, the principle of competitive learning is used to develop an iterative algorithm for image recovery and segmentation. Within the framework of Markov Random Fields, the image recovery problem is transformed to the problem of minimization of an energy function. A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. Relationship of the update rule to Kohonen&#39;s update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. Simulation experiments using this algorithm on real and synthetic images show promising results on smoothing within regions and also on enhancing the boundaries. Restoration results computer favorably with recently published results using Markov Random Fields and mean field approximation.</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="117458191"><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="117458191"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458191; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117458191]").text(description); $(".js-view-count[data-work-id=117458191]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117458191; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117458191']"); 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" } } $('.js-work-strip[data-work-id=117458191]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117458191,"title":"\u003ctitle\u003eImage recovery and segmentation using competitive learning in a layered network\u003c/title\u003e","translated_title":"","metadata":{"abstract":"In this study, the principle of competitive learning is used to develop an iterative algorithm for image recovery and segmentation. Within the framework of Markov Random Fields, the image recovery problem is transformed to the problem of minimization of an energy function. A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. Relationship of the update rule to Kohonen\u0026#39;s update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. Simulation experiments using this algorithm on real and synthetic images show promising results on smoothing within regions and also on enhancing the boundaries. Restoration results computer favorably with recently published results using Markov Random Fields and mean field approximation.","publisher":"SPIE","publication_date":{"day":29,"month":10,"year":1993,"errors":{}},"publication_name":"Proceedings of SPIE"},"translated_abstract":"In this study, the principle of competitive learning is used to develop an iterative algorithm for image recovery and segmentation. Within the framework of Markov Random Fields, the image recovery problem is transformed to the problem of minimization of an energy function. A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. Relationship of the update rule to Kohonen\u0026#39;s update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. Simulation experiments using this algorithm on real and synthetic images show promising results on smoothing within regions and also on enhancing the boundaries. Restoration results computer favorably with recently published results using Markov Random Fields and mean field approximation.","internal_url":"https://www.academia.edu/117458191/_title_Image_recovery_and_segmentation_using_competitive_learning_in_a_layered_network_title_","translated_internal_url":"","created_at":"2024-04-13T20:44:35.469-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"_title_Image_recovery_and_segmentation_using_competitive_learning_in_a_layered_network_title_","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"In this study, the principle of competitive learning is used to develop an iterative algorithm for image recovery and segmentation. Within the framework of Markov Random Fields, the image recovery problem is transformed to the problem of minimization of an energy function. A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. Relationship of the update rule to Kohonen\u0026#39;s update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. Simulation experiments using this algorithm on real and synthetic images show promising results on smoothing within regions and also on enhancing the boundaries. Restoration results computer favorably with recently published results using Markov Random Fields and mean field approximation.","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"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":26870,"name":"Image segmentation","url":"https://www.academia.edu/Documents/in/Image_segmentation"},{"id":225304,"name":"Smoothing","url":"https://www.academia.edu/Documents/in/Smoothing"}],"urls":[{"id":41074163,"url":"https://doi.org/10.1117/12.162052"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="117458190"><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/117458190/Securing_dynamic_microgrid_partition_in_the_smart_grid"><img alt="Research paper thumbnail of Securing dynamic microgrid partition in the smart grid" class="work-thumbnail" src="https://attachments.academia-assets.com/113310143/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/117458190/Securing_dynamic_microgrid_partition_in_the_smart_grid">Securing dynamic microgrid partition in the smart grid</a></div><div class="wp-workCard_item"><span>International Journal of Distributed Sensor Networks</span><span>, May 1, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Message authentication has vital significance for dynamic microgrid partition in smart grid. Howe...</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">Message authentication has vital significance for dynamic microgrid partition in smart grid. However, current message authentication protocols based on ''public key infrastructure'' are too complicated to be deployed in smart grid and lack group information management function. On the other hand, group information management protocols based on ''logic key hierarchy'' need to broadcast a lot of messages during microgrid partition processes, resulting in high communication costs. To address these issues, we present a novel identity-based message authentication protocol for dynamic microgrid partition called securing dynamic microgrid partition. Similar to the protocols of this field, securing dynamic microgrid partition can provide message authentication and group information management functions. However, compared to other well-known approaches, securing dynamic microgrid partition uses Bloom filter for managing group information, which can reduce the communication cost of logic key hierarchy significantly. Moreover, securing dynamic microgrid partition uses Lagrange interpolation for designing new identity-based signing and verification algorithms, which is simple to be deployed in smart grid environment and much more efficient than current identity-based protocols. Experimental results show that the proposed approach is feasible for real-world applications.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e512d6eb221adbdb8c1d77461381d347" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113310143,"asset_id":117458190,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113310143/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="117458190"><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="117458190"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458190; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117458190]").text(description); $(".js-view-count[data-work-id=117458190]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117458190; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117458190']"); 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: "e512d6eb221adbdb8c1d77461381d347" } } $('.js-work-strip[data-work-id=117458190]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117458190,"title":"Securing dynamic microgrid partition in the smart grid","translated_title":"","metadata":{"publisher":"Hindawi Publishing Corporation","grobid_abstract":"Message authentication has vital significance for dynamic microgrid partition in smart grid. However, current message authentication protocols based on ''public key infrastructure'' are too complicated to be deployed in smart grid and lack group information management function. On the other hand, group information management protocols based on ''logic key hierarchy'' need to broadcast a lot of messages during microgrid partition processes, resulting in high communication costs. To address these issues, we present a novel identity-based message authentication protocol for dynamic microgrid partition called securing dynamic microgrid partition. Similar to the protocols of this field, securing dynamic microgrid partition can provide message authentication and group information management functions. However, compared to other well-known approaches, securing dynamic microgrid partition uses Bloom filter for managing group information, which can reduce the communication cost of logic key hierarchy significantly. Moreover, securing dynamic microgrid partition uses Lagrange interpolation for designing new identity-based signing and verification algorithms, which is simple to be deployed in smart grid environment and much more efficient than current identity-based protocols. Experimental results show that the proposed approach is feasible for real-world applications.","publication_date":{"day":1,"month":5,"year":2017,"errors":{}},"publication_name":"International Journal of Distributed Sensor Networks","grobid_abstract_attachment_id":113310143},"translated_abstract":null,"internal_url":"https://www.academia.edu/117458190/Securing_dynamic_microgrid_partition_in_the_smart_grid","translated_internal_url":"","created_at":"2024-04-13T20:44:35.306-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":113310143,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113310143/thumbnails/1.jpg","file_name":"1550147717711136.pdf","download_url":"https://www.academia.edu/attachments/113310143/download_file","bulk_download_file_name":"Securing_dynamic_microgrid_partition_in.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113310143/1550147717711136-libre.pdf?1713067225=\u0026response-content-disposition=attachment%3B+filename%3DSecuring_dynamic_microgrid_partition_in.pdf\u0026Expires=1742122266\u0026Signature=A7MiZTzJ1Z4Xfw7tjWTTKwQY7Jj8RO07Zye~9a5iiG7OzcYnhP~bIjVrEQj2BldTtKbUBkM4gT8U43vfoIlFU7Yn~5w5hlDrOIbGQ1G~ytH7ftNJH8SA8301cL5UKls00Ipnx765N-Mf~HHJtPq1fecVsDHQ7pMyOdzTySVvf4XzBvw7sikOIsjTOTxs5sgKZpTCbtxK2ZNlzlA9PL4esJyIr0bprL9r3DZP4sN8WMAviAOzN48q214LyuwXdlai7BO6Mh9bmRvXzAYq0Brvnv~2GnGID5jdjdKrQnKB9LyYYPiJCNxUY7d4RjrTzKnGOFaboOnNbT7IBXXTMhLnxw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Securing_dynamic_microgrid_partition_in_the_smart_grid","translated_slug":"","page_count":12,"language":"en","content_type":"Work","summary":"Message authentication has vital significance for dynamic microgrid partition in smart grid. However, current message authentication protocols based on ''public key infrastructure'' are too complicated to be deployed in smart grid and lack group information management function. On the other hand, group information management protocols based on ''logic key hierarchy'' need to broadcast a lot of messages during microgrid partition processes, resulting in high communication costs. To address these issues, we present a novel identity-based message authentication protocol for dynamic microgrid partition called securing dynamic microgrid partition. Similar to the protocols of this field, securing dynamic microgrid partition can provide message authentication and group information management functions. However, compared to other well-known approaches, securing dynamic microgrid partition uses Bloom filter for managing group information, which can reduce the communication cost of logic key hierarchy significantly. Moreover, securing dynamic microgrid partition uses Lagrange interpolation for designing new identity-based signing and verification algorithms, which is simple to be deployed in smart grid environment and much more efficient than current identity-based protocols. Experimental results show that the proposed approach is feasible for real-world applications.","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[{"id":113310143,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113310143/thumbnails/1.jpg","file_name":"1550147717711136.pdf","download_url":"https://www.academia.edu/attachments/113310143/download_file","bulk_download_file_name":"Securing_dynamic_microgrid_partition_in.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113310143/1550147717711136-libre.pdf?1713067225=\u0026response-content-disposition=attachment%3B+filename%3DSecuring_dynamic_microgrid_partition_in.pdf\u0026Expires=1742122266\u0026Signature=A7MiZTzJ1Z4Xfw7tjWTTKwQY7Jj8RO07Zye~9a5iiG7OzcYnhP~bIjVrEQj2BldTtKbUBkM4gT8U43vfoIlFU7Yn~5w5hlDrOIbGQ1G~ytH7ftNJH8SA8301cL5UKls00Ipnx765N-Mf~HHJtPq1fecVsDHQ7pMyOdzTySVvf4XzBvw7sikOIsjTOTxs5sgKZpTCbtxK2ZNlzlA9PL4esJyIr0bprL9r3DZP4sN8WMAviAOzN48q214LyuwXdlai7BO6Mh9bmRvXzAYq0Brvnv~2GnGID5jdjdKrQnKB9LyYYPiJCNxUY7d4RjrTzKnGOFaboOnNbT7IBXXTMhLnxw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":113310144,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113310144/thumbnails/1.jpg","file_name":"1550147717711136.pdf","download_url":"https://www.academia.edu/attachments/113310144/download_file","bulk_download_file_name":"Securing_dynamic_microgrid_partition_in.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113310144/1550147717711136-libre.pdf?1713067228=\u0026response-content-disposition=attachment%3B+filename%3DSecuring_dynamic_microgrid_partition_in.pdf\u0026Expires=1742122266\u0026Signature=YncZJcdGBoprsgaSbaS1iEsnCbKJD3CjlOd9fkMPXZTqWr~N7HdoKUvSM2c7Vpzr-ZJ3fyH-k6ZgaBpElXiI8S94zR0EDoJ9l2qLP1s0xxqNOm28XO7Au6J3Bv4Bs~nSKu-J1NUTj0LYKp63i4s6UbH9SF2-3~ULAkYXSpBsFSjBzzAdkqZZDtRTVAKcsPzUKWFK2bBlskBbR908Zd~NKwgfKWPQjrKQ4G5K2gTVi3Y1JKfpZhOBHRBh8rxvE8ywWd7yUHM3uZqv1RpXx0mdlTfs92qT5bB-lRDaTmmxMlj3uBEdbAWbEwlEcpmqOEb9rrU8pLBxrcHFzO5MgWg4aQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science"},{"id":422,"name":"Computer 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":26364,"name":"Smart Grid","url":"https://www.academia.edu/Documents/in/Smart_Grid"},{"id":38643,"name":"Microgrid","url":"https://www.academia.edu/Documents/in/Microgrid"},{"id":303727,"name":"Grid","url":"https://www.academia.edu/Documents/in/Grid"}],"urls":[{"id":41074162,"url":"https://journals.sagepub.com/doi/pdf/10.1177/1550147717711136"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="117458189"><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/117458189/Image_recovery_and_segmentation_using_competitive_learning_in_a_computational_network"><img alt="Research paper thumbnail of Image recovery and segmentation using competitive learning in a computational network" class="work-thumbnail" src="https://attachments.academia-assets.com/113310167/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/117458189/Image_recovery_and_segmentation_using_competitive_learning_in_a_computational_network">Image recovery and segmentation using competitive learning in a computational network</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In 1 his study, we have used the principle of competitive learning to develop an iterative algori...</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 1 his study, we have used the principle of competitive learning to develop an iterative algorithm for image recovery and segmentation. Within the framework of Markov random fields (MRF's), 1 he image recovery problem is transformed to the problem of minimization of an energy function. A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. The relationship of the update rule to Kohonen's update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. As compared to recently published results using mean field approximation, our algorithm shows consistently better performance in edge preservation and comparable performance in enhancing within the boundaries. These results are based on simulation experiments on a set of synthetic images corrupted by Gaussian noise and on a set of real images.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="94751fab7b8039e8e77c254b65a1fb07" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113310167,"asset_id":117458189,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113310167/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="117458189"><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="117458189"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458189; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117458189]").text(description); $(".js-view-count[data-work-id=117458189]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117458189; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117458189']"); 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: "94751fab7b8039e8e77c254b65a1fb07" } } $('.js-work-strip[data-work-id=117458189]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117458189,"title":"Image recovery and segmentation using competitive learning in a computational network","translated_title":"","metadata":{"ai_title_tag":"Competitive Learning for Image Recovery and Segmentation","grobid_abstract":"In 1 his study, we have used the principle of competitive learning to develop an iterative algorithm for image recovery and segmentation. 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If the monitoring of a dangerous environment is required, then one may not be able to deploy a wired network.</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="117458188"><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="117458188"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458188; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117458188]").text(description); $(".js-view-count[data-work-id=117458188]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117458188; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117458188']"); 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" } } $('.js-work-strip[data-work-id=117458188]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117458188,"title":"Coverage and Connectivity","translated_title":"","metadata":{"abstract":"A major advantage of wireless sensor networks (WSNs) over wired networks is the potential for ad hoc deployment of the network. 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The fact that the Government formulated or supplied the drawings, specifications, or other data does not license the holder or any other person or corporation; or convey any rights or permission to manufacture, use, or sell any patented invention that may relate to them. This report is the result of contracted fundamental research deemed exempt from public affairs security and policy review in accordance with SAF/AQR memorandum dated 10 Dec 08 and AFRL/CA policy clarification memorandum dated 16 Jan 09. This report is available to the general public, including foreign nationals. 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Copies may be obtained from the Defense Technical Information Center (DTIC) (http://www.dtic.mil).","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[{"id":113310155,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113310155/thumbnails/1.jpg","file_name":"6f3bddaafe0b6949d8edcae6eec585eecbc8.pdf","download_url":"https://www.academia.edu/attachments/113310155/download_file","bulk_download_file_name":"Context_Aware_Active_Authentication_usin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113310155/6f3bddaafe0b6949d8edcae6eec585eecbc8-libre.pdf?1713067258=\u0026response-content-disposition=attachment%3B+filename%3DContext_Aware_Active_Authentication_usin.pdf\u0026Expires=1742100734\u0026Signature=LV303nL50BbNdZU8ghTV~uG~9uKVfZjFWLY7MkXY1pVXAC1NCHxNVK40u5r2JwaXkorAWNsn2A1Z7aAFJw7pd8oe7BBn5Q3pqL88aNF5-NatbKEGXeXuguICeUH88tvfBT0AlstvlAKgfJbpiBkT~ogoViqdta7CTsqCcJS~leQ9RWq4R3mV2RmdZzZEUk8RaplvcIFlrz~DpxrADpyPIws4h8bwnNWpM~aT3YWk49xZkPzW-4gZcZTHKumOdAOEQ3ZKB0~vginEur~uyEqqOwtjQ4~mMxS~VQMb4iunsz5aOqqLyYOYYtRU6bhrQ9ioolAwt6uEMTg53WQdk6w3lQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":859,"name":"Communication","url":"https://www.academia.edu/Documents/in/Communication"},{"id":3147,"name":"Gesture","url":"https://www.academia.edu/Documents/in/Gesture"},{"id":184366,"name":"TYPING","url":"https://www.academia.edu/Documents/in/TYPING"}],"urls":[{"id":41074159,"url":"https://doi.org/10.21236/ad1005650"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="117458186"><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/117458186/An_Adaptive_Web_Cache_Access_Predictor_Using_Neural_Network"><img alt="Research paper thumbnail of An Adaptive Web Cache Access Predictor Using Neural Network" class="work-thumbnail" src="https://attachments.academia-assets.com/113310154/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/117458186/An_Adaptive_Web_Cache_Access_Predictor_Using_Neural_Network">An Adaptive Web Cache Access Predictor Using Neural Network</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2002</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents a novel approach to successfully predict Web pages that are most likely to be...</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 paper presents a novel approach to successfully predict Web pages that are most likely to be re-accessed in a given period of time. We present the design of an intelligent predictor that can be implemented on a Web server to guide caching strategies. Our approach is adaptive and learns the changing access patterns of pages in a Web site. The core of our predictor is a neural network that uses a back-propagation learning rule. We present results of the application of this predictor on static data using log files; it can be extended to learn the distribution of live Web page access patterns. Our simulations show fast learning, uniformly good prediction, and up to 82% correct prediction for the following six months based on a oneday training data. This long-range prediction accuracy is attributed to the static structure of the test Web site.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0b231cf8b1f99a58b3ae29c117c82240" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113310154,"asset_id":117458186,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113310154/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="117458186"><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="117458186"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458186; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117458186]").text(description); $(".js-view-count[data-work-id=117458186]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117458186; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117458186']"); 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: "0b231cf8b1f99a58b3ae29c117c82240" } } $('.js-work-strip[data-work-id=117458186]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117458186,"title":"An Adaptive Web Cache Access Predictor Using Neural Network","translated_title":"","metadata":{"publisher":"Springer Science+Business Media","grobid_abstract":"This paper presents a novel approach to successfully predict Web pages that are most likely to be re-accessed in a given period of time. 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This long-range prediction accuracy is attributed to the static structure of the test Web site.","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[{"id":113310154,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113310154/thumbnails/1.jpg","file_name":"ben_20choi_202002_20on_20lecture_20notes_20in_20artificial_20intelligence.pdf","download_url":"https://www.academia.edu/attachments/113310154/download_file","bulk_download_file_name":"An_Adaptive_Web_Cache_Access_Predictor_U.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113310154/ben_20choi_202002_20on_20lecture_20notes_20in_20artificial_20intelligence-libre.pdf?1713067220=\u0026response-content-disposition=attachment%3B+filename%3DAn_Adaptive_Web_Cache_Access_Predictor_U.pdf\u0026Expires=1742122266\u0026Signature=bD9Ziql5u2H9buOdLDRsKZJTonBVgHVKlhrIApN4vMHxaK58bsH1n5oP-oxXil8~cHRKHPjxenWObHOrzDyxRDaD4oAR2bmQxrAMVz-euSQEOdeQ9Iiv-ssBoRekD2Um-IcE6K1uvkXIMKdKvXUGAGyhloaBrI72uMTVYfwYpT5Ssiq9jQHbhaHMoSYya4au4v4NvAA2f2U7xE0lkQselr0t-4~XF2EhJSA5gOJhqgP~17tSbOrtlLB8YLMWV~pNBM2hkGNojBwqGBnlQFpMzlIKUXZVGYQk5lPyaQgjFcm0BF~2xzEqfZzdi8Ld5vrOWiNKKUSxNdwXPOt8HXInng__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"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":5750,"name":"Back Propagation","url":"https://www.academia.edu/Documents/in/Back_Propagation"},{"id":13413,"name":"Web page design","url":"https://www.academia.edu/Documents/in/Web_page_design"},{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network"},{"id":135799,"name":"Web Caching","url":"https://www.academia.edu/Documents/in/Web_Caching"},{"id":148995,"name":"Long Range","url":"https://www.academia.edu/Documents/in/Long_Range"},{"id":216672,"name":"Cache","url":"https://www.academia.edu/Documents/in/Cache"},{"id":481229,"name":"Web Pages","url":"https://www.academia.edu/Documents/in/Web_Pages"},{"id":806573,"name":"Web Server","url":"https://www.academia.edu/Documents/in/Web_Server"},{"id":979025,"name":"Log Files","url":"https://www.academia.edu/Documents/in/Log_Files"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"},{"id":1671808,"name":"Prediction Accuracy","url":"https://www.academia.edu/Documents/in/Prediction_Accuracy"}],"urls":[{"id":41074158,"url":"https://doi.org/10.1007/3-540-48035-8_44"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="117458185"><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/117458185/Wireless_Sensor_Networks"><img alt="Research paper thumbnail of Wireless Sensor Networks" class="work-thumbnail" src="https://attachments.academia-assets.com/113310153/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/117458185/Wireless_Sensor_Networks">Wireless Sensor Networks</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper, we propose and evaluate a distributed, energy-efficient, lightweight framework for...</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 this paper, we propose and evaluate a distributed, energy-efficient, lightweight framework for target localization and tracking in wireless sensor networks. Since radio communication is the most energy-consuming operation, this framework aims to reduce the number of messages and the number of message collisions, while providing refined accuracy. The key element of the framework is a novel localization algorithm, called Ratiometric Vector Iteration (RVI). RVI is based on distance ratio estimates rather than absolute distance estimates which are often impossible to calculate. By iteratively updating the estimated location using the distance ratio, RVI localizes the target accurately with only three sensors' participation. After localization, the location of the target is reported to the subscriber. If the target is stationary or moves around within a small area, it is wasteful to report (almost) the same location estimates repeatedly. We, therefore, propose to dynamically adjust a reporting frequency considering the target's movement so that we can reduce the number of report messages while maintaining tracking quality. Extensive simulation results show that the proposed framework combining RVI and the movement-adaptive report scheduling algorithm reduces the localization error and total number of the transmitted messages up to half of those of the existing approaches.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="054483641df3f05be80fb99492c7f9d6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113310153,"asset_id":117458185,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113310153/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="117458185"><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="117458185"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458185; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117458185]").text(description); $(".js-view-count[data-work-id=117458185]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117458185; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117458185']"); 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: "054483641df3f05be80fb99492c7f9d6" } } $('.js-work-strip[data-work-id=117458185]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117458185,"title":"Wireless Sensor Networks","translated_title":"","metadata":{"ai_title_tag":"Energy-Efficient Target Localization in Wireless Sensor Networks","grobid_abstract":"In this paper, we propose and evaluate a distributed, energy-efficient, lightweight framework for target localization and tracking in wireless sensor networks. Since radio communication is the most energy-consuming operation, this framework aims to reduce the number of messages and the number of message collisions, while providing refined accuracy. The key element of the framework is a novel localization algorithm, called Ratiometric Vector Iteration (RVI). RVI is based on distance ratio estimates rather than absolute distance estimates which are often impossible to calculate. By iteratively updating the estimated location using the distance ratio, RVI localizes the target accurately with only three sensors' participation. After localization, the location of the target is reported to the subscriber. If the target is stationary or moves around within a small area, it is wasteful to report (almost) the same location estimates repeatedly. We, therefore, propose to dynamically adjust a reporting frequency considering the target's movement so that we can reduce the number of report messages while maintaining tracking quality. Extensive simulation results show that the proposed framework combining RVI and the movement-adaptive report scheduling algorithm reduces the localization error and total number of the transmitted messages up to half of those of the existing approaches.","publication_date":{"day":27,"month":9,"year":2010,"errors":{}},"grobid_abstract_attachment_id":113310153},"translated_abstract":null,"internal_url":"https://www.academia.edu/117458185/Wireless_Sensor_Networks","translated_internal_url":"","created_at":"2024-04-13T20:44:33.905-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":113310153,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113310153/thumbnails/1.jpg","file_name":"localization_comcom2006.pdf","download_url":"https://www.academia.edu/attachments/113310153/download_file","bulk_download_file_name":"Wireless_Sensor_Networks.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113310153/localization_comcom2006-libre.pdf?1713067218=\u0026response-content-disposition=attachment%3B+filename%3DWireless_Sensor_Networks.pdf\u0026Expires=1742122266\u0026Signature=c~VFV7U4QKmq7biDHIwtAbz3I0U1MabKvCRUkbf2m0NHDMOsKodCQ7jNaKtFTcTc9IwnatMNHzWnbNkleDoAEwtlnG6tufNX87AdGAxjY1Wm5ajTr79DXbVuRbggNYR5mAvXZKkpht6bhuIg4XgB274l5HGpOleXgySkpcCvqfkoGdtAJ7gTaX~v8o7JXT7wcg~R33jUzWR57~PZ1RRcZCKzaKyXfzEPe9ePUiN7wokDzEB1fr6n0U-qI2sHHo7H9V7Ic9DjhXel1SOcpKpm1quST~s894gpNTVEAXc1SWbXgYTDzYC2YJ4UMurLYy7vURkfq92FWiiuQlytyRQNRQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Wireless_Sensor_Networks","translated_slug":"","page_count":12,"language":"en","content_type":"Work","summary":"In this paper, we propose and evaluate a distributed, energy-efficient, lightweight framework for target localization and tracking in wireless sensor networks. Since radio communication is the most energy-consuming operation, this framework aims to reduce the number of messages and the number of message collisions, while providing refined accuracy. The key element of the framework is a novel localization algorithm, called Ratiometric Vector Iteration (RVI). RVI is based on distance ratio estimates rather than absolute distance estimates which are often impossible to calculate. By iteratively updating the estimated location using the distance ratio, RVI localizes the target accurately with only three sensors' participation. After localization, the location of the target is reported to the subscriber. If the target is stationary or moves around within a small area, it is wasteful to report (almost) the same location estimates repeatedly. We, therefore, propose to dynamically adjust a reporting frequency considering the target's movement so that we can reduce the number of report messages while maintaining tracking quality. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="117458182"><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/117458182/Using_power_law_properties_of_social_groups_for_cloud_defense_and_community_detection"><img alt="Research paper thumbnail of Using power-law properties of social groups for cloud defense and community detection" class="work-thumbnail" src="https://attachments.academia-assets.com/113310142/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/117458182/Using_power_law_properties_of_social_groups_for_cloud_defense_and_community_detection">Using power-law properties of social groups for cloud defense and community detection</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The power-law distribution can be used to describe various aspects of social group behavior. For ...</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">The power-law distribution can be used to describe various aspects of social group behavior. For mussels, sociobiological research has shown that the Levy walk best describes their self-organizing movement strategy. A mussel&#39;s step length is drawn from a power-law distribution, and its direction is drawn from a uniform distribution. In the area of social networks, theories such as preferential attachment seek to explain why the degree distribution tends to be scale-free. The aim of this dissertation is to glean insight from these works to help solve problems in two domains: cloud computing systems and community detection. Privacy and security are two areas of concern for cloud systems. Recent research has provided evidence indicating how a malicious user could perform co-residence profiling and public to private IP mapping to target and exploit customers which share physical resources. This work proposes a defense strategy, in part inspired by mussel self-organization, that reli...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e9ff71fa7fdd014d33e46d393c8aac4a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113310142,"asset_id":117458182,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113310142/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="117458182"><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="117458182"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458182; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=117458182]").text(description); $(".js-view-count[data-work-id=117458182]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 117458182; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='117458182']"); 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: "e9ff71fa7fdd014d33e46d393c8aac4a" } } $('.js-work-strip[data-work-id=117458182]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":117458182,"title":"Using power-law properties of social groups for cloud defense and community detection","translated_title":"","metadata":{"abstract":"The power-law distribution can be used to describe various aspects of social group behavior. For mussels, sociobiological research has shown that the Levy walk best describes their self-organizing movement strategy. A mussel\u0026#39;s step length is drawn from a power-law distribution, and its direction is drawn from a uniform distribution. In the area of social networks, theories such as preferential attachment seek to explain why the degree distribution tends to be scale-free. The aim of this dissertation is to glean insight from these works to help solve problems in two domains: cloud computing systems and community detection. Privacy and security are two areas of concern for cloud systems. Recent research has provided evidence indicating how a malicious user could perform co-residence profiling and public to private IP mapping to target and exploit customers which share physical resources. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="117458176"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/117458176/Fuzzy_Evaluation_of_Agent_Based_Semantic_Match_Making_Algorithm_for_Cyberspace"><img alt="Research paper thumbnail of Fuzzy Evaluation of Agent-Based Semantic Match-Making Algorithm for Cyberspace" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/117458176/Fuzzy_Evaluation_of_Agent_Based_Semantic_Match_Making_Algorithm_for_Cyberspace">Fuzzy Evaluation of Agent-Based Semantic Match-Making Algorithm for Cyberspace</a></div><div class="wp-workCard_item"><span>Social Science Research Network</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="117458176"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="117458176"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 117458176; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112753182"><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/112753182/Continuous_Authentication_Using_One_class_Classifiers_and_their_Fusion"><img alt="Research paper thumbnail of Continuous Authentication Using One-class Classifiers and their Fusion" class="work-thumbnail" src="https://attachments.academia-assets.com/109886355/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/112753182/Continuous_Authentication_Using_One_class_Classifiers_and_their_Fusion">Continuous Authentication Using One-class Classifiers and their Fusion</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Oct 30, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">While developing continuous authentication systems (CAS), we generally assume that samples from b...</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">While developing continuous authentication systems (CAS), we generally assume that samples from both genuine and impostor classes are readily available. However, the assumption may not be true in certain circumstances. Therefore, we explore the possibility of implementing CAS using only genuine samples. Specifically, we investigate the usefulness of four one-class classifiers OCC (elliptic envelope, isolation forest, local outliers factor, and one-class support vector machines) and their fusion. The performance of these classifiers was evaluated on four distinct behavioral biometric datasets, and compared with eight multi-class classifiers (M CC). The results demonstrate that if we have sufficient training data from the genuine user the OCC, and their fusion can closely match the performance of the majority of M CC. Our findings encourage the research community to use OCC in order to build CAS as they do not require knowledge of impostor class during the enrollment process.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c53f080261eb7de0f2a05fa1f95e89d5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":109886355,"asset_id":112753182,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/109886355/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="112753182"><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="112753182"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112753182; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112753182]").text(description); $(".js-view-count[data-work-id=112753182]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112753182; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112753182']"); 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: "c53f080261eb7de0f2a05fa1f95e89d5" } } $('.js-work-strip[data-work-id=112753182]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112753182,"title":"Continuous Authentication Using One-class Classifiers and their Fusion","translated_title":"","metadata":{"publisher":"Cornell University","ai_title_tag":"One-Class Classifiers for Continuous Authentication","grobid_abstract":"While developing continuous authentication systems (CAS), we generally assume that samples from both genuine and impostor classes are readily available. 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Our findings encourage the research community to use OCC in order to build CAS as they do not require knowledge of impostor class during the enrollment process.","publication_date":{"day":30,"month":10,"year":2017,"errors":{}},"publication_name":"arXiv (Cornell University)","grobid_abstract_attachment_id":109886355},"translated_abstract":null,"internal_url":"https://www.academia.edu/112753182/Continuous_Authentication_Using_One_class_Classifiers_and_their_Fusion","translated_internal_url":"","created_at":"2024-01-01T19:02:56.339-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":109886355,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109886355/thumbnails/1.jpg","file_name":"1710.pdf","download_url":"https://www.academia.edu/attachments/109886355/download_file","bulk_download_file_name":"Continuous_Authentication_Using_One_clas.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109886355/1710-libre.pdf?1704164632=\u0026response-content-disposition=attachment%3B+filename%3DContinuous_Authentication_Using_One_clas.pdf\u0026Expires=1742122266\u0026Signature=TkPdHnfpLZBIe6RlRwfqrutRRE3GZ9Hpp1FBqUrYBamq0dFOf774knghKD6UTbFjYIt3ifKbEZkqvQFXao1M4KD4hBakt4Q2yxHyj0W68urVlaFc~L1k3vNvykGAhJTtJ02LN9x7wAPUjaA2Neg03IXtZ1MWDtRT501Zf6SMOIbTKctRfZNJrh4eYUZMgj2QKm83AE6EkHN~Nq~ZXx6oxLQZyHDvu3w2Gi36xp6DFQHtVUqbDVRlkoKmNAmvPJGv17Vr~-jHe9zZHkdtomPS2~v55AeKySKeMKpZGSnPL1vnINJR98U4vcGvacc8Fj2YyMicUbbOBLkMArRWlJ0Q5g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Continuous_Authentication_Using_One_class_Classifiers_and_their_Fusion","translated_slug":"","page_count":8,"language":"en","content_type":"Work","summary":"While developing continuous authentication systems (CAS), we generally assume that samples from both genuine and impostor classes are readily available. However, the assumption may not be true in certain circumstances. Therefore, we explore the possibility of implementing CAS using only genuine samples. Specifically, we investigate the usefulness of four one-class classifiers OCC (elliptic envelope, isolation forest, local outliers factor, and one-class support vector machines) and their fusion. The performance of these classifiers was evaluated on four distinct behavioral biometric datasets, and compared with eight multi-class classifiers (M CC). The results demonstrate that if we have sufficient training data from the genuine user the OCC, and their fusion can closely match the performance of the majority of M CC. Our findings encourage the research community to use OCC in order to build CAS as they do not require knowledge of impostor class during the enrollment process.","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[{"id":109886355,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/109886355/thumbnails/1.jpg","file_name":"1710.pdf","download_url":"https://www.academia.edu/attachments/109886355/download_file","bulk_download_file_name":"Continuous_Authentication_Using_One_clas.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/109886355/1710-libre.pdf?1704164632=\u0026response-content-disposition=attachment%3B+filename%3DContinuous_Authentication_Using_One_clas.pdf\u0026Expires=1742122266\u0026Signature=TkPdHnfpLZBIe6RlRwfqrutRRE3GZ9Hpp1FBqUrYBamq0dFOf774knghKD6UTbFjYIt3ifKbEZkqvQFXao1M4KD4hBakt4Q2yxHyj0W68urVlaFc~L1k3vNvykGAhJTtJ02LN9x7wAPUjaA2Neg03IXtZ1MWDtRT501Zf6SMOIbTKctRfZNJrh4eYUZMgj2QKm83AE6EkHN~Nq~ZXx6oxLQZyHDvu3w2Gi36xp6DFQHtVUqbDVRlkoKmNAmvPJGv17Vr~-jHe9zZHkdtomPS2~v55AeKySKeMKpZGSnPL1vnINJR98U4vcGvacc8Fj2YyMicUbbOBLkMArRWlJ0Q5g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"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":9173,"name":"Biometrics","url":"https://www.academia.edu/Documents/in/Biometrics"},{"id":191289,"name":"Support vector machine","url":"https://www.academia.edu/Documents/in/Support_vector_machine"},{"id":1941885,"name":"Outlier","url":"https://www.academia.edu/Documents/in/Outlier"}],"urls":[{"id":38060607,"url":"http://arxiv.org/pdf/1710.11075"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112753181"><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/112753181/A_Non_interactive_Dual_channel_Authentication_Protocol_for_Assuring_Pseudo_confidentiality"><img alt="Research paper thumbnail of A Non-interactive Dual-channel Authentication Protocol for Assuring Pseudo-confidentiality" 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"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/112753181/A_Non_interactive_Dual_channel_Authentication_Protocol_for_Assuring_Pseudo_confidentiality">A Non-interactive Dual-channel Authentication Protocol for Assuring Pseudo-confidentiality</a></div><div class="wp-workCard_item"><span>Network and Distributed System Security Symposium</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We introduce a non-interactive dual channel authentication protocol and apply it to long distance...</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">We introduce a non-interactive dual channel authentication protocol and apply it to long distance communication for assuring pseudo-confidentiality, a criteria that prevents a malicious agent from exfiltrating information to unauthorized destinations. Unlike previously proposed protocols that assume a manual (human-aided) or equivalent authenticated channel, our protocol utilizes a non-manual authenticated channel. We analyze the security properties for a possible realization of this protocol and develop a prototype. Through a Raspberry-Pi implementation, we show how the incorporation of the proposed scheme into the future design of keyboard interfaces may impact authentication practices. 1. Non-interactive dual channel authentication protocols Non-interactive dual channel authentication protocols employ two channels and authenticate information r1 received through one presumably insecure channel using a piece of brief information r2 (computed from r1) received through the other presumably authenticated channel. To remain lightweight these protocols employ one way communication from a message sender, Alice, to a message recipient, Bob. It can be shown that in these protocols, imposters cannot authenticate themselves if, (i) only one of the channels is compromised or (ii) both channels are compromised but attacks on them are not coordinated. Existing literature describes a family of non-interactive message authentication protocols (e.g., [1, 2, 4, 3, 5]), known as NIMAPs, that use a manual (human-aided) authenticated channel. In these protocols, a hash value for each message being sent is generated. Alice then transmits This work is supported by AFOSR Grants FA9550-09-1-0479 and FA 9550-09-1-0165. the message and hash over the insecure and authenticated channels respectively to Bob. In some protocols, a key is applied to the hash function when generating the hash value and this key is sent with the message over the insecure channel [3, 5] or with the hash value over the authenticated channel [2]. NIMAPs assume that the authenticated channel is human-aided hence adversarial influence on this channel is significantly limited. This assumption is sufficient for short distance communications requiring infrequent authentication of messages. However, this limits their application in long distance communications (e.g, over the internet) where frequent authentication is required and a human-aided authenticated channel is unrealistic and/or infeasible. 2. Proposed protocol Replacement of the human-aided authenticated channel in NIMAPs erodes the security benefit assured by such a channel. This exposes them (NIMAPs) to channel spoof attacks where an adversary, Eve, could spoof the identity of the authenticated channel when sending her authentication messages to Bob. Because of the non-interactive nature of Figure 1. Proposed protocol (Generic) the protocol, Bob has no way to know whether these authentications are sent by Alice hence, he will accept a message if its authentication is valid (i.e., it authenticates the message sent through the insecure channel). We address this issue by introducing the following: (i) we assign the task of Alice to a small hardware attachment that we assume not to be affected by a channel compromise, and (ii) we assume that it is difficult for the adversary, Eve to compute r2 given r1. Figure 1 presents the proposed generic protocol. In this protocol, Alice is a hardware attachment to the keyboard with the following properties: (i) She has processing capabilities to parse typed keystrokes and apply a method generate to identify r1 and to compute r2 from r1; (ii) She only receives input from the keyboard; (iii) She sends output to Bob using two different channels (one is insecure that runs through the host and the other is authenticated that bypasses the host). Bob is a verifier program located in a remote computer. In Figure 1 (and in subsequent protocol figures), the “′” added to information received by Bob indicates that the sent and received values might be different and the insecure and authenticated channels are represented by “→” and “⇒” respectively. The protocol is designed based on the following assumptions: (i) an r2 can verify the associated r1; (ii) an adversary can generate an r1 or snoop the r1 generated by Alice but it is difficult for her to compute the associated r2 from a given r1; (iii) Bob can compute r2 from r1; (iv) r′ 1 is accepted only if (r′ 1, r′ 2) is consistent i.e., r2b (the expected r′ 2 computed by Bob for the r′ 1 he received) is the same as r′ 2. Alice generates r1 and r2 from the keystrokes typed by a user and sends r1 through the possibly compromised host using the insecure channel and r2 directly to Bob using the authenticated channel. When Bob receives r1 and r2 from Alice, he accepts r′ 1 only if (r′ 1, r′ 2) is consistent. To defeat this protocol, Eve either; (i) waits for Alice to transmit an r1 and replaces r1 with r1eve and expects that it will be…</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="112753181"><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="112753181"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112753181; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112753181]").text(description); $(".js-view-count[data-work-id=112753181]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112753181; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112753181']"); 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" } } $('.js-work-strip[data-work-id=112753181]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112753181,"title":"A Non-interactive Dual-channel Authentication Protocol for Assuring Pseudo-confidentiality","translated_title":"","metadata":{"abstract":"We introduce a non-interactive dual channel authentication protocol and apply it to long distance communication for assuring pseudo-confidentiality, a criteria that prevents a malicious agent from exfiltrating information to unauthorized destinations. Unlike previously proposed protocols that assume a manual (human-aided) or equivalent authenticated channel, our protocol utilizes a non-manual authenticated channel. We analyze the security properties for a possible realization of this protocol and develop a prototype. Through a Raspberry-Pi implementation, we show how the incorporation of the proposed scheme into the future design of keyboard interfaces may impact authentication practices. 1. Non-interactive dual channel authentication protocols Non-interactive dual channel authentication protocols employ two channels and authenticate information r1 received through one presumably insecure channel using a piece of brief information r2 (computed from r1) received through the other presumably authenticated channel. To remain lightweight these protocols employ one way communication from a message sender, Alice, to a message recipient, Bob. It can be shown that in these protocols, imposters cannot authenticate themselves if, (i) only one of the channels is compromised or (ii) both channels are compromised but attacks on them are not coordinated. Existing literature describes a family of non-interactive message authentication protocols (e.g., [1, 2, 4, 3, 5]), known as NIMAPs, that use a manual (human-aided) authenticated channel. In these protocols, a hash value for each message being sent is generated. Alice then transmits This work is supported by AFOSR Grants FA9550-09-1-0479 and FA 9550-09-1-0165. the message and hash over the insecure and authenticated channels respectively to Bob. In some protocols, a key is applied to the hash function when generating the hash value and this key is sent with the message over the insecure channel [3, 5] or with the hash value over the authenticated channel [2]. NIMAPs assume that the authenticated channel is human-aided hence adversarial influence on this channel is significantly limited. This assumption is sufficient for short distance communications requiring infrequent authentication of messages. However, this limits their application in long distance communications (e.g, over the internet) where frequent authentication is required and a human-aided authenticated channel is unrealistic and/or infeasible. 2. Proposed protocol Replacement of the human-aided authenticated channel in NIMAPs erodes the security benefit assured by such a channel. This exposes them (NIMAPs) to channel spoof attacks where an adversary, Eve, could spoof the identity of the authenticated channel when sending her authentication messages to Bob. Because of the non-interactive nature of Figure 1. Proposed protocol (Generic) the protocol, Bob has no way to know whether these authentications are sent by Alice hence, he will accept a message if its authentication is valid (i.e., it authenticates the message sent through the insecure channel). We address this issue by introducing the following: (i) we assign the task of Alice to a small hardware attachment that we assume not to be affected by a channel compromise, and (ii) we assume that it is difficult for the adversary, Eve to compute r2 given r1. Figure 1 presents the proposed generic protocol. In this protocol, Alice is a hardware attachment to the keyboard with the following properties: (i) She has processing capabilities to parse typed keystrokes and apply a method generate to identify r1 and to compute r2 from r1; (ii) She only receives input from the keyboard; (iii) She sends output to Bob using two different channels (one is insecure that runs through the host and the other is authenticated that bypasses the host). Bob is a verifier program located in a remote computer. In Figure 1 (and in subsequent protocol figures), the “′” added to information received by Bob indicates that the sent and received values might be different and the insecure and authenticated channels are represented by “→” and “⇒” respectively. The protocol is designed based on the following assumptions: (i) an r2 can verify the associated r1; (ii) an adversary can generate an r1 or snoop the r1 generated by Alice but it is difficult for her to compute the associated r2 from a given r1; (iii) Bob can compute r2 from r1; (iv) r′ 1 is accepted only if (r′ 1, r′ 2) is consistent i.e., r2b (the expected r′ 2 computed by Bob for the r′ 1 he received) is the same as r′ 2. Alice generates r1 and r2 from the keystrokes typed by a user and sends r1 through the possibly compromised host using the insecure channel and r2 directly to Bob using the authenticated channel. When Bob receives r1 and r2 from Alice, he accepts r′ 1 only if (r′ 1, r′ 2) is consistent. To defeat this protocol, Eve either; (i) waits for Alice to transmit an r1 and replaces r1 with r1eve and expects that it will be…","publication_date":{"day":null,"month":null,"year":2013,"errors":{}},"publication_name":"Network and Distributed System Security Symposium"},"translated_abstract":"We introduce a non-interactive dual channel authentication protocol and apply it to long distance communication for assuring pseudo-confidentiality, a criteria that prevents a malicious agent from exfiltrating information to unauthorized destinations. Unlike previously proposed protocols that assume a manual (human-aided) or equivalent authenticated channel, our protocol utilizes a non-manual authenticated channel. We analyze the security properties for a possible realization of this protocol and develop a prototype. Through a Raspberry-Pi implementation, we show how the incorporation of the proposed scheme into the future design of keyboard interfaces may impact authentication practices. 1. Non-interactive dual channel authentication protocols Non-interactive dual channel authentication protocols employ two channels and authenticate information r1 received through one presumably insecure channel using a piece of brief information r2 (computed from r1) received through the other presumably authenticated channel. To remain lightweight these protocols employ one way communication from a message sender, Alice, to a message recipient, Bob. It can be shown that in these protocols, imposters cannot authenticate themselves if, (i) only one of the channels is compromised or (ii) both channels are compromised but attacks on them are not coordinated. Existing literature describes a family of non-interactive message authentication protocols (e.g., [1, 2, 4, 3, 5]), known as NIMAPs, that use a manual (human-aided) authenticated channel. In these protocols, a hash value for each message being sent is generated. Alice then transmits This work is supported by AFOSR Grants FA9550-09-1-0479 and FA 9550-09-1-0165. the message and hash over the insecure and authenticated channels respectively to Bob. In some protocols, a key is applied to the hash function when generating the hash value and this key is sent with the message over the insecure channel [3, 5] or with the hash value over the authenticated channel [2]. NIMAPs assume that the authenticated channel is human-aided hence adversarial influence on this channel is significantly limited. This assumption is sufficient for short distance communications requiring infrequent authentication of messages. However, this limits their application in long distance communications (e.g, over the internet) where frequent authentication is required and a human-aided authenticated channel is unrealistic and/or infeasible. 2. Proposed protocol Replacement of the human-aided authenticated channel in NIMAPs erodes the security benefit assured by such a channel. This exposes them (NIMAPs) to channel spoof attacks where an adversary, Eve, could spoof the identity of the authenticated channel when sending her authentication messages to Bob. Because of the non-interactive nature of Figure 1. Proposed protocol (Generic) the protocol, Bob has no way to know whether these authentications are sent by Alice hence, he will accept a message if its authentication is valid (i.e., it authenticates the message sent through the insecure channel). We address this issue by introducing the following: (i) we assign the task of Alice to a small hardware attachment that we assume not to be affected by a channel compromise, and (ii) we assume that it is difficult for the adversary, Eve to compute r2 given r1. Figure 1 presents the proposed generic protocol. In this protocol, Alice is a hardware attachment to the keyboard with the following properties: (i) She has processing capabilities to parse typed keystrokes and apply a method generate to identify r1 and to compute r2 from r1; (ii) She only receives input from the keyboard; (iii) She sends output to Bob using two different channels (one is insecure that runs through the host and the other is authenticated that bypasses the host). Bob is a verifier program located in a remote computer. In Figure 1 (and in subsequent protocol figures), the “′” added to information received by Bob indicates that the sent and received values might be different and the insecure and authenticated channels are represented by “→” and “⇒” respectively. The protocol is designed based on the following assumptions: (i) an r2 can verify the associated r1; (ii) an adversary can generate an r1 or snoop the r1 generated by Alice but it is difficult for her to compute the associated r2 from a given r1; (iii) Bob can compute r2 from r1; (iv) r′ 1 is accepted only if (r′ 1, r′ 2) is consistent i.e., r2b (the expected r′ 2 computed by Bob for the r′ 1 he received) is the same as r′ 2. Alice generates r1 and r2 from the keystrokes typed by a user and sends r1 through the possibly compromised host using the insecure channel and r2 directly to Bob using the authenticated channel. When Bob receives r1 and r2 from Alice, he accepts r′ 1 only if (r′ 1, r′ 2) is consistent. To defeat this protocol, Eve either; (i) waits for Alice to transmit an r1 and replaces r1 with r1eve and expects that it will be…","internal_url":"https://www.academia.edu/112753181/A_Non_interactive_Dual_channel_Authentication_Protocol_for_Assuring_Pseudo_confidentiality","translated_internal_url":"","created_at":"2024-01-01T19:02:56.156-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"A_Non_interactive_Dual_channel_Authentication_Protocol_for_Assuring_Pseudo_confidentiality","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"We introduce a non-interactive dual channel authentication protocol and apply it to long distance communication for assuring pseudo-confidentiality, a criteria that prevents a malicious agent from exfiltrating information to unauthorized destinations. Unlike previously proposed protocols that assume a manual (human-aided) or equivalent authenticated channel, our protocol utilizes a non-manual authenticated channel. We analyze the security properties for a possible realization of this protocol and develop a prototype. Through a Raspberry-Pi implementation, we show how the incorporation of the proposed scheme into the future design of keyboard interfaces may impact authentication practices. 1. Non-interactive dual channel authentication protocols Non-interactive dual channel authentication protocols employ two channels and authenticate information r1 received through one presumably insecure channel using a piece of brief information r2 (computed from r1) received through the other presumably authenticated channel. To remain lightweight these protocols employ one way communication from a message sender, Alice, to a message recipient, Bob. It can be shown that in these protocols, imposters cannot authenticate themselves if, (i) only one of the channels is compromised or (ii) both channels are compromised but attacks on them are not coordinated. Existing literature describes a family of non-interactive message authentication protocols (e.g., [1, 2, 4, 3, 5]), known as NIMAPs, that use a manual (human-aided) authenticated channel. In these protocols, a hash value for each message being sent is generated. Alice then transmits This work is supported by AFOSR Grants FA9550-09-1-0479 and FA 9550-09-1-0165. the message and hash over the insecure and authenticated channels respectively to Bob. In some protocols, a key is applied to the hash function when generating the hash value and this key is sent with the message over the insecure channel [3, 5] or with the hash value over the authenticated channel [2]. NIMAPs assume that the authenticated channel is human-aided hence adversarial influence on this channel is significantly limited. This assumption is sufficient for short distance communications requiring infrequent authentication of messages. However, this limits their application in long distance communications (e.g, over the internet) where frequent authentication is required and a human-aided authenticated channel is unrealistic and/or infeasible. 2. Proposed protocol Replacement of the human-aided authenticated channel in NIMAPs erodes the security benefit assured by such a channel. This exposes them (NIMAPs) to channel spoof attacks where an adversary, Eve, could spoof the identity of the authenticated channel when sending her authentication messages to Bob. Because of the non-interactive nature of Figure 1. Proposed protocol (Generic) the protocol, Bob has no way to know whether these authentications are sent by Alice hence, he will accept a message if its authentication is valid (i.e., it authenticates the message sent through the insecure channel). We address this issue by introducing the following: (i) we assign the task of Alice to a small hardware attachment that we assume not to be affected by a channel compromise, and (ii) we assume that it is difficult for the adversary, Eve to compute r2 given r1. Figure 1 presents the proposed generic protocol. In this protocol, Alice is a hardware attachment to the keyboard with the following properties: (i) She has processing capabilities to parse typed keystrokes and apply a method generate to identify r1 and to compute r2 from r1; (ii) She only receives input from the keyboard; (iii) She sends output to Bob using two different channels (one is insecure that runs through the host and the other is authenticated that bypasses the host). Bob is a verifier program located in a remote computer. In Figure 1 (and in subsequent protocol figures), the “′” added to information received by Bob indicates that the sent and received values might be different and the insecure and authenticated channels are represented by “→” and “⇒” respectively. The protocol is designed based on the following assumptions: (i) an r2 can verify the associated r1; (ii) an adversary can generate an r1 or snoop the r1 generated by Alice but it is difficult for her to compute the associated r2 from a given r1; (iii) Bob can compute r2 from r1; (iv) r′ 1 is accepted only if (r′ 1, r′ 2) is consistent i.e., r2b (the expected r′ 2 computed by Bob for the r′ 1 he received) is the same as r′ 2. Alice generates r1 and r2 from the keystrokes typed by a user and sends r1 through the possibly compromised host using the insecure channel and r2 directly to Bob using the authenticated channel. When Bob receives r1 and r2 from Alice, he accepts r′ 1 only if (r′ 1, r′ 2) is consistent. To defeat this protocol, Eve either; (i) waits for Alice to transmit an r1 and replaces r1 with r1eve and expects that it will be…","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":13923,"name":"Computer Security","url":"https://www.academia.edu/Documents/in/Computer_Security"},{"id":62540,"name":"Computer Network","url":"https://www.academia.edu/Documents/in/Computer_Network"},{"id":196193,"name":"Confidentiality","url":"https://www.academia.edu/Documents/in/Confidentiality"},{"id":2176821,"name":"Authentication Protocol","url":"https://www.academia.edu/Documents/in/Authentication_Protocol"},{"id":3273604,"name":"Dual (grammatical number)","url":"https://www.academia.edu/Documents/in/Dual_grammatical_number_"}],"urls":[{"id":38060606,"url":"https://dblp.uni-trier.de/db/conf/ndss/ndss2013.html#IrakizaKP13"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112753180"><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/112753180/Looking_Through_Your_Smartphone_Screen_to_Steal_Your_Pin_Using_a_3D_Camera"><img alt="Research paper thumbnail of Looking Through Your Smartphone Screen to Steal Your Pin Using a 3D Camera" 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"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/112753180/Looking_Through_Your_Smartphone_Screen_to_Steal_Your_Pin_Using_a_3D_Camera">Looking Through Your Smartphone Screen to Steal Your Pin Using a 3D Camera</a></div><div class="wp-workCard_item"><span>Advances in intelligent systems and computing</span><span>, Nov 2, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Recent research shows that video recordings of the user’s hand movement and his or her smartphone...</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">Recent research shows that video recordings of the user’s hand movement and his or her smartphone screen display can be used to steal sensitive information such as pins and passwords. The methods presented in the past assume the victim to be present in a well illuminated place. In this paper, we present a novel attack on the smartphone users’ pins that does not require a highly illuminated room and works even in the complete darkness. We use a DS325 Soft Kinect camera to record the users’ interaction with their smartphones while they type their pins. Using the 900 short RGBD video recording of the pin entry process from 30 different users, we show our attack was able to break 43% of the pins in the first attempt and 61% of the pins in the first 10 attempts. With the advancements in the quality and accessibility of the depth-sensing cameras day by day, we believe our work exposes a major security risk in the present and future and calls the community to take a closer look at the security measures for the usage of the smart devices.</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="112753180"><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="112753180"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112753180; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112753180]").text(description); $(".js-view-count[data-work-id=112753180]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112753180; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112753180']"); 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" } } $('.js-work-strip[data-work-id=112753180]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112753180,"title":"Looking Through Your Smartphone Screen to Steal Your Pin Using a 3D Camera","translated_title":"","metadata":{"abstract":"Recent research shows that video recordings of the user’s hand movement and his or her smartphone screen display can be used to steal sensitive information such as pins and passwords. The methods presented in the past assume the victim to be present in a well illuminated place. In this paper, we present a novel attack on the smartphone users’ pins that does not require a highly illuminated room and works even in the complete darkness. We use a DS325 Soft Kinect camera to record the users’ interaction with their smartphones while they type their pins. Using the 900 short RGBD video recording of the pin entry process from 30 different users, we show our attack was able to break 43% of the pins in the first attempt and 61% of the pins in the first 10 attempts. With the advancements in the quality and accessibility of the depth-sensing cameras day by day, we believe our work exposes a major security risk in the present and future and calls the community to take a closer look at the security measures for the usage of the smart devices.","publisher":"Springer Nature","publication_date":{"day":2,"month":11,"year":2018,"errors":{}},"publication_name":"Advances in intelligent systems and computing"},"translated_abstract":"Recent research shows that video recordings of the user’s hand movement and his or her smartphone screen display can be used to steal sensitive information such as pins and passwords. The methods presented in the past assume the victim to be present in a well illuminated place. In this paper, we present a novel attack on the smartphone users’ pins that does not require a highly illuminated room and works even in the complete darkness. We use a DS325 Soft Kinect camera to record the users’ interaction with their smartphones while they type their pins. Using the 900 short RGBD video recording of the pin entry process from 30 different users, we show our attack was able to break 43% of the pins in the first attempt and 61% of the pins in the first 10 attempts. With the advancements in the quality and accessibility of the depth-sensing cameras day by day, we believe our work exposes a major security risk in the present and future and calls the community to take a closer look at the security measures for the usage of the smart devices.","internal_url":"https://www.academia.edu/112753180/Looking_Through_Your_Smartphone_Screen_to_Steal_Your_Pin_Using_a_3D_Camera","translated_internal_url":"","created_at":"2024-01-01T19:02:55.947-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Looking_Through_Your_Smartphone_Screen_to_Steal_Your_Pin_Using_a_3D_Camera","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Recent research shows that video recordings of the user’s hand movement and his or her smartphone screen display can be used to steal sensitive information such as pins and passwords. The methods presented in the past assume the victim to be present in a well illuminated place. In this paper, we present a novel attack on the smartphone users’ pins that does not require a highly illuminated room and works even in the complete darkness. We use a DS325 Soft Kinect camera to record the users’ interaction with their smartphones while they type their pins. Using the 900 short RGBD video recording of the pin entry process from 30 different users, we show our attack was able to break 43% of the pins in the first attempt and 61% of the pins in the first 10 attempts. With the advancements in the quality and accessibility of the depth-sensing cameras day by day, we believe our work exposes a major security risk in the present and future and calls the community to take a closer look at the security measures for the usage of the smart devices.","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":13923,"name":"Computer Security","url":"https://www.academia.edu/Documents/in/Computer_Security"},{"id":433921,"name":"Password","url":"https://www.academia.edu/Documents/in/Password"}],"urls":[{"id":38060605,"url":"https://doi.org/10.1007/978-3-030-01177-2_73"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112753179"><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/112753179/Security_in_WSNs"><img alt="Research paper thumbnail of Security in WSNs" 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"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/112753179/Security_in_WSNs">Security in WSNs</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Security in wireless sensor networks (WSNs) is centered on six fundamental requirements namely, a...</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">Security in wireless sensor networks (WSNs) is centered on six fundamental requirements namely, authentication, confidentiality, integrity, reliability, availability and data freshness [4, 39, 52, 62]. In this chapter, we describe these requirements, the different kinds of attacks that aim to compromise these requirements (and hence the security of a WSN) and the defense mechanisms that can be employed to overcome these attacks.</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="112753179"><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="112753179"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112753179; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112753179]").text(description); $(".js-view-count[data-work-id=112753179]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112753179; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112753179']"); 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" } } $('.js-work-strip[data-work-id=112753179]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112753179,"title":"Security in WSNs","translated_title":"","metadata":{"abstract":"Security in wireless sensor networks (WSNs) is centered on six fundamental requirements namely, authentication, confidentiality, integrity, reliability, availability and data freshness [4, 39, 52, 62]. 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In this chapter, we describe these requirements, the different kinds of attacks that aim to compromise these requirements (and hence the security of a WSN) and the defense mechanisms that can be employed to overcome these attacks.","internal_url":"https://www.academia.edu/112753179/Security_in_WSNs","translated_internal_url":"","created_at":"2024-01-01T19:02:55.751-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":31955997,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Security_in_WSNs","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"Security in wireless sensor networks (WSNs) is centered on six fundamental requirements namely, authentication, confidentiality, integrity, reliability, availability and data freshness [4, 39, 52, 62]. In this chapter, we describe these requirements, the different kinds of attacks that aim to compromise these requirements (and hence the security of a WSN) and the defense mechanisms that can be employed to overcome these attacks.","owner":{"id":31955997,"first_name":"Vir","middle_initials":"","last_name":"Phoha","page_name":"VirPhoha","domain_name":"syr","created_at":"2015-06-07T08:48:20.079-07:00","display_name":"Vir Phoha","url":"https://syr.academia.edu/VirPhoha"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":9136,"name":"Wireless Sensor Networks","url":"https://www.academia.edu/Documents/in/Wireless_Sensor_Networks"},{"id":13923,"name":"Computer Security","url":"https://www.academia.edu/Documents/in/Computer_Security"},{"id":116557,"name":"Compromise","url":"https://www.academia.edu/Documents/in/Compromise"},{"id":196193,"name":"Confidentiality","url":"https://www.academia.edu/Documents/in/Confidentiality"},{"id":341805,"name":"Data Integrity","url":"https://www.academia.edu/Documents/in/Data_Integrity"},{"id":1211847,"name":"Wireless Sensor Network","url":"https://www.academia.edu/Documents/in/Wireless_Sensor_Network"}],"urls":[{"id":38060604,"url":"https://doi.org/10.1007/978-3-319-46769-6_4"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112753177"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/112753177/Self_repair_and_adaptation_in_collective_and_parallel_computational_networks_a_statistical_approximation"><img alt="Research paper thumbnail of Self-repair and adaptation in collective and parallel computational networks: a statistical approximation" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/112753177/Self_repair_and_adaptation_in_collective_and_parallel_computational_networks_a_statistical_approximation">Self-repair and adaptation in collective and parallel computational networks: a statistical approximation</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="112753177"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="112753177"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112753177; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="112753175"><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/112753175/Wireless_Sensor_Networks_Security_Coverage_and_Localization"><img alt="Research paper thumbnail of Wireless Sensor Networks: Security, Coverage, and Localization" 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"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/112753175/Wireless_Sensor_Networks_Security_Coverage_and_Localization">Wireless Sensor Networks: Security, Coverage, and Localization</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This book presents a comprehensive overview of wireless sensor networks (WSNs) with an emphasis o...</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 book presents a comprehensive overview of wireless sensor networks (WSNs) with an emphasis on security, coverage, and localization. It offers a structural treatment of WSN building blocks including hardware and protocol architectures and also provides a systems-level view of how WSNs operate. These building blocks will allow readers to program specialized applications and conduct research in advanced topics. A brief introductory chapter covers common applications and communication protocols for WSNs. Next, the authors review basic mathematical models such as Voroni diagrams and Delaunay triangulations. Sensor principles, hardware structure, and medium access protocols are examined. Security challenges ranging from defense strategies to network robustness are explored, along with quality of service measures. Finally, this book discusses recent developments and future directions in WSN platforms. Each chapter concludes with classroom-tested exercises that reinforce key concepts. This book is suitable for researchers and for practitioners in industry. Advanced-level students in electrical engineering and computer science will also find the content helpful as a textbook or 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="112753175"><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="112753175"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 112753175; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=112753175]").text(description); $(".js-view-count[data-work-id=112753175]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 112753175; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='112753175']"); 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" } } $('.js-work-strip[data-work-id=112753175]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":112753175,"title":"Wireless Sensor Networks: Security, Coverage, and Localization","translated_title":"","metadata":{"abstract":"This book presents a comprehensive overview of wireless sensor networks (WSNs) with an emphasis on security, coverage, and localization. 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In this work, we show through a combination of supervised learning methods and statistical tests, that there are certain users for whom exploitation of information of how a phone is held drastically improves classification performance. We propose a two-stage authentication framework that identifies the location of the phone before performing authentication, and show its benefits based on a data-set of 55 users. Our work represents a major step towards bridging the gap between accelerometer-based authentication systems analyzed from the context of a laboratory environment and a real accelerometer-based authentication system in the wild where phone positioning cannot be assumed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f0ead469eeb3bb8f9b6d38bcecf14a34" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":34727018,"asset_id":8319619,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/34727018/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="8319619"><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="8319619"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 8319619; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=8319619]").text(description); $(".js-view-count[data-work-id=8319619]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 8319619; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='8319619']"); 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: "f0ead469eeb3bb8f9b6d38bcecf14a34" } } $('.js-work-strip[data-work-id=8319619]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":8319619,"title":"Context-Aware Active Authentication Using Smartphone Accelerometer Measurements","translated_title":"","metadata":{"abstract":"While body movement patterns recorded by a smartphone accelerometer are now well understood to be discriminative enough to separate users, little work has been done to address the question of if or how the position in which the phone is held affects user authentication. 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Implemented on a data-set of 200 videos of the PIN entry process on an HTC One phone, we show, that the attack breaks an average of over 50% of the PINs on the first attempt and an average of over 85% of the PINs in ten attempts. Because the attack can be conducted in such a way not to raise suspicion (i.e., since the adversary does not have to direct the camera at the screen), we believe that it is very likely to be adopted by adversaries who seek to stealthily steal sensitive private information. As users conduct more and more of their computing transactions on mobile devices in the open, the paper calls for tech community to take a closer look at the risks posed by the now ubiquitous camera-enabled devices.                                  <br /> <br />Final version of this paper is accepted for publicaion at ACM Computer and Communication Security (CCS 2014), August 2014.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bda3afa362d596d3b5b6cfa5a93876ad" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":35013536,"asset_id":8319636,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/35013536/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="8319636"><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="8319636"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 8319636; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=8319636]").text(description); $(".js-view-count[data-work-id=8319636]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 8319636; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='8319636']"); 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: "bda3afa362d596d3b5b6cfa5a93876ad" } } $('.js-work-strip[data-work-id=8319636]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":8319636,"title":"Beware, Your Hands Reveal Your Secrets!","translated_title":"","metadata":{"abstract":"Research on attacks which exploit video-based side-channels to decode text typed on a smartphone has traditionally assumed that the adversary is able to leverage some information from the screen display (say, a reflection of the screen or a low resolution video of the content typed on the screen).This paper introduces a new breed of side-channel attack on the PIN entry process on a smartphone which entirely relies on the spatio-temporal dynamics of the hands during typing to decode the typed text. 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