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(PDF) An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques
<!DOCTYPE html> <html > <head> <meta charset="utf-8"> <meta rel="search" type="application/opensearchdescription+xml" href="/open_search.xml" title="Academia.edu"> <meta content="width=device-width, initial-scale=1" name="viewport"> <meta name="google-site-verification" content="bKJMBZA7E43xhDOopFZkssMMkBRjvYERV-NaN4R6mrs"> <meta name="csrf-param" content="authenticity_token" /> <meta name="csrf-token" content="DneC4q5OKQYYLFx1c1rmikD1IGizUCS6Qk43P3DJYEOPDakBiSethAlW_bJ6NTc2Wds4Av2x9nylUFort6PsdQ" /> <meta name="citation_title" content="An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques" /> <meta name="citation_publication_date" content="2022/04/25" /> <meta name="citation_journal_title" content="Computational Intelligence and Neuroscience" /> <meta name="citation_author" content="Nabeel Albishry" /> <meta name="citation_author" content="Rayed AlGhamdi" /> <meta name="citation_author" content="Abdulmohsen Almalawi" /> <meta name="citation_author" content="Asif Irshad Khan" /> <meta name="citation_author" content="Pravin R. Kshirsagar" /> <meta name="citation_author" content="BaruDebtera" /> <meta name="citation_volume" content="2022" /> <meta name="citation_firstpage" content="1-13" /> <meta name="citation_issn" content="1687-5273" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/79653026/An_Attribute_Extraction_for_Automated_Malware_Attack_Classification_and_Detection_Using_Soft_Computing_Techniques" /> <meta name="twitter:title" content="An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques" /> <meta name="twitter:description" content="Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as" /> <meta name="twitter:image" content="https://0.academia-photos.com/22658377/6554680/22152649/s200_baru.debtera.jpg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/79653026/An_Attribute_Extraction_for_Automated_Malware_Attack_Classification_and_Detection_Using_Soft_Computing_Techniques" /> <meta property="og:title" content="An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as" /> <meta property="article:author" content="https://acadamia.academia.edu/BaruDebtera" /> <meta name="description" content="Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as" /> <title>(PDF) An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques</title> <link rel="canonical" href="https://www.academia.edu/79653026/An_Attribute_Extraction_for_Automated_Malware_Attack_Classification_and_Detection_Using_Soft_Computing_Techniques" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = 'b092bf3a3df71cf13feee7c143e83a57eb6b94fb'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1739840302000); window.Aedu.timeDifference = new Date().getTime() - 1739840302000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as feasible. This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection. The findings indicated that merging PCA attribute extraction and SVM classifier results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods.","author":[{"@context":"https://schema.org","@type":"Person","name":"Baru Debtera","url":"https://acadamia.academia.edu/BaruDebtera"}],"contributor":[],"dateCreated":"2022-05-22","dateModified":"2025-01-06","datePublished":"2022-04-25","headline":"An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques","image":"https://attachments.academia-assets.com/86294556/thumbnails/1.jpg","inLanguage":"en","keywords":["Computer Science","Artificial Intelligence","Machine Learning","Malware","Support vector machine"],"publication":"Computational Intelligence and Neuroscience","publisher":{"@context":"https://schema.org","@type":"Organization","name":"Hindawi 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window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":79653026,"created_at":"2022-05-22T07:19:46.307-07:00","from_world_paper_id":205929511,"updated_at":"2024-11-24T20:55:32.786-08:00","_data":{"publisher":"Hindawi Limited","grobid_abstract":"Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as feasible. is article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classi cation and detection. e ndings indicated that merging PCA attribute extraction and SVM classi er results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods.","publication_date":"2022,4,25","publication_name":"Computational Intelligence and Neuroscience","grobid_abstract_attachment_id":"86294556"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [22658377]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":86294556,"attachmentType":"pdf"}"><img alt="First page of “An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/86294556/mini_magick20220522-9030-gh8kdm.png?1653229249" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="22658377" href="https://acadamia.academia.edu/BaruDebtera"><img alt="Profile image of Baru Debtera" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/22658377/6554680/22152649/s65_baru.debtera.jpg" />Baru Debtera</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2022, Computational Intelligence and Neuroscience</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">13 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 79653026; 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To preserve networks, information, and intelligence, malware must be detected as soon as feasible. is article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classi cation and detection. e ndings indicated that merging PCA attribute extraction and SVM classi er results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":86294556,"attachmentType":"pdf","workUrl":"https://www.academia.edu/79653026/An_Attribute_Extraction_for_Automated_Malware_Attack_Classification_and_Detection_Using_Soft_Computing_Techniques"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":86294556,"attachmentType":"pdf","workUrl":"https://www.academia.edu/79653026/An_Attribute_Extraction_for_Automated_Malware_Attack_Classification_and_Detection_Using_Soft_Computing_Techniques"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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Analysis recommends that the consequences of malware are getting worse. Though we know that malware has had a exceptional significance on the world. The growing number of computer security incidents suggests that malware is contagious. Surfing the internet without antivirus installed and firewall protection enabled should induce any reader of the extensive and malicious nature of malware. For determining malwares, a system known as a malware detector is used which attempts to conclude whether a program has malicious objective. In order to evade uncovering, malware writers (hackers) habitually use obfuscation to morph malware. To solve the problem of malware detection a number of pattern recognition and machine learning algorithms has been proposed. The paper states the problem of classifier fusion with soft labels for Malware Detection. Performance of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) is presented here. The performance of fusing these classifiers using approaches based on Dempster-Shafer Theory, Average Bayes Combination and Neural Network is proposed. We also investigate extracting network level features for malwares and analyze the performance of classifiers when these features are included.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Ensemble of Soft Computing Techniques for Malware detection","attachmentId":32540768,"attachmentType":"pdf","work_url":"https://www.academia.edu/5406204/Ensemble_of_Soft_Computing_Techniques_for_Malware_detection","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/5406204/Ensemble_of_Soft_Computing_Techniques_for_Malware_detection"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="87386560" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/87386560/PROVIDING_CYBER_SECURITY_SOLUTION_FOR_MALWARE_DETECTION_USING_SUPPORT_VECTOR_MACHINE_ALGORITHM_SVM">PROVIDING CYBER SECURITY SOLUTION FOR MALWARE DETECTION USING SUPPORT VECTOR MACHINE ALGORITHM (SVM</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="31493941" href="https://irjet.academia.edu/IRJET">IRJET Journal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IRJET, 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">Malware detection developers faced an issue with a generation of recent signatures of malware code. A very famous and recognized technique is the pattern-based malware code detection technique. This results in the evasion of signatures that are built to support the code syntax. During this paper, we discuss some well-known methods of malware detection supported by the semantic feature extraction technique. In the current decade, most of the authors focused on the malware feature extraction process for the generic detection process. The effectiveness of the Malicious Sequence Pattern Matching technique for malware detection invites moderation and improvement of the present system and method. Some authors used the rule mining technique, another used the graph technique and a few also focused on the feature clustering process of malware detection. The focus of the Multi-Classification framework is to detect the malicious affected files. To protect legitimate users from attacks, the foremost significant line of defense against malware is antimalware software products, which mainly use signature-based methods for detection. Machine Learning algorithms are proved useful at identifying zero-day attacks or detecting an unusual behavior of systems that might indicate an attack or malware.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"PROVIDING CYBER SECURITY SOLUTION FOR MALWARE DETECTION USING SUPPORT VECTOR MACHINE ALGORITHM (SVM","attachmentId":91609332,"attachmentType":"pdf","work_url":"https://www.academia.edu/87386560/PROVIDING_CYBER_SECURITY_SOLUTION_FOR_MALWARE_DETECTION_USING_SUPPORT_VECTOR_MACHINE_ALGORITHM_SVM","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/87386560/PROVIDING_CYBER_SECURITY_SOLUTION_FOR_MALWARE_DETECTION_USING_SUPPORT_VECTOR_MACHINE_ALGORITHM_SVM"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="54093928" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/54093928/Review_of_Soft_Computing_in_Malware_Detection">Review of Soft Computing in Malware Detection</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="10747617" href="https://independent.academia.edu/RavinderSingla">Ravinder Singla</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Special issues on IP Multimedia Communications, 2011</p><p class="ds-related-work--abstract ds2-5-body-sm">Soft computing techniques are widely used in malware detection in these days. These techniques have the ability of learning from the past incidences and can categories normal and abnormal behaviour. In this paper we have reviewed various soft computing techniques. A review of application of these softcomputing techniques in malware detection has also been presented in this paper. Despite so much research, techniques with good accuracy and low false alarm rate are still needs attention.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Review of Soft Computing in Malware Detection","attachmentId":70624940,"attachmentType":"pdf","work_url":"https://www.academia.edu/54093928/Review_of_Soft_Computing_in_Malware_Detection","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/54093928/Review_of_Soft_Computing_in_Malware_Detection"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="6507831" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/6507831/Analysis_of_Machine_learning_Techniques_Used_in_Behavior_Based_MalwareDetection">Analysis of Machine learning Techniques Used in Behavior-Based MalwareDetection</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="10379897" href="https://sgu-id.academia.edu/AlvaErwin">Alva Erwin</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2010</p><p class="ds-related-work--abstract ds2-5-body-sm">The increase of malware that are exploiting the Internet daily has become a serious threat. The manual heuristic inspection of malware analysis is no longer considered effective and efficient compared against the high spreading rate of malware. Hence, automated behavior-based malware detection using machine learning techniques is considered a profound solution. The behavior of each malware on an emulated (sandbox) environment will be automatically analyzed and will generate behavior reports. These reports will be preprocessed into sparse vector models for further machine learning (classification). The classifiers used in this research are k-Nearest</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Analysis of Machine learning Techniques Used in Behavior-Based MalwareDetection","attachmentId":48836309,"attachmentType":"pdf","work_url":"https://www.academia.edu/6507831/Analysis_of_Machine_learning_Techniques_Used_in_Behavior_Based_MalwareDetection","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/6507831/Analysis_of_Machine_learning_Techniques_Used_in_Behavior_Based_MalwareDetection"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="54807451" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/54807451/Malware_Detection_Supportive_Software_Agents_and_Its_Classification_Schemes">Malware Detection, Supportive Software Agents and Its Classification Schemes</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="4980592" href="https://futminna.academia.edu/OlawaleAdebayo">Olawale Adebayo</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Over time, the task of curbing the emergence of malware and its dastard activities has been identified in terms of analysis, detection and containment of malware. Malware is a general term that is used to describe the category of malicious software that is part of security threats to the computer and internet system. It is a malignant program designed to hamper the effectiveness of a computer and internet system. This paper aims at identifying the malware as one of the most dreaded threats to an emerging computer and communication technology. The paper identified the category of malware, malware classification algorithms, malwares activities and ways of preventing and removing malware if it eventually infects system. The research also describes tools that classify malware dataset using a rule-based classification scheme and machine learning algorithms to detect the malicious program from normal program through pattern recognition.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Malware Detection, Supportive Software Agents and Its Classification Schemes","attachmentId":70991683,"attachmentType":"pdf","work_url":"https://www.academia.edu/54807451/Malware_Detection_Supportive_Software_Agents_and_Its_Classification_Schemes","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/54807451/Malware_Detection_Supportive_Software_Agents_and_Its_Classification_Schemes"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="5587303" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/5587303/Nugroho_Analysis_of_Machine_learning_Techniques_Used_in_Behavior_Based_Malware_Detection_2010">Nugroho - Analysis of Machine learning Techniques Used in Behavior-Based Malware Detection - 2010</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="7968594" href="https://independent.academia.edu/AhmadKelixoRamirez">Ahmad Kelixo Ramirez</a></div><p class="ds-related-work--abstract ds2-5-body-sm">The increase of malware that are exploiting the Internet daily has become a serious threat. The manual heuristic inspection of malware analysis is no longer considered effective and efficient compared against the high spreading rate of malware. Hence, automated behavior-based malware detection using machine learning techniques is considered a profound solution. The behavior of each malware on an emulated (sandbox) environment will be automatically analyzed and will generate behavior reports. These reports will be preprocessed into sparse vector models for further machine learning (classification). The classifiers used in this research are k-Nearest Neighbors (kNN), Naïve Bayes, J48 Decision Tree, Support Vector Machine (SVM), and Multilayer Perceptron Neural Network (MLP). Based on the analysis of the tests and experimental results of all the 5 classifiers, the overall best performance was achieved by J48 decision tree with a recall of 95.9%, a false positive rate of 2.4%, a precision of 97.3%, and an accuracy of 96.8%. In summary, it can be concluded that a proofof-concept based on automatic behavior-based malware analysis and the use of machine learning techniques could detect malware quite effectively and efficiently.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Nugroho - Analysis of Machine learning Techniques Used in Behavior-Based Malware Detection - 2010","attachmentId":32669981,"attachmentType":"pdf","work_url":"https://www.academia.edu/5587303/Nugroho_Analysis_of_Machine_learning_Techniques_Used_in_Behavior_Based_Malware_Detection_2010","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/5587303/Nugroho_Analysis_of_Machine_learning_Techniques_Used_in_Behavior_Based_Malware_Detection_2010"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="68976481" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/68976481/A_Study_on_the_Malware_Analysis_with_Machine_Learning_Methods">A Study on the Malware Analysis with Machine Learning Methods</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="198512755" href="https://independent.academia.edu/SmtESwathiAssistantProfessor">Smt.E.Swathi Assistant Professor</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2019</p><p class="ds-related-work--abstract ds2-5-body-sm">Current days, malware made by attackers are usually polymorphic in nature. Polymorphic malware is a kind of malware that regularly transforms its recognizable functions in order to trick discovery making use of normal signature-based versions [4]. Behavior-based malware discovery assesses not simply on the trademark of the documents yet likewise based upon the activity it intends to plan that is likewise prior to it really carries out that habits. This job offers advised methods for artificial intelligence based malware category as well as discovery, in addition to the standards for its execution. Additionally, the research can be valuable as a base for more study in the area of malware analysis with artificial intelligence methods.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Study on the Malware Analysis with Machine Learning Methods","attachmentId":79254130,"attachmentType":"pdf","work_url":"https://www.academia.edu/68976481/A_Study_on_the_Malware_Analysis_with_Machine_Learning_Methods","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/68976481/A_Study_on_the_Malware_Analysis_with_Machine_Learning_Methods"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="92674519" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/92674519/A_Machine_Learning_Technique_to_Detect_Malware">A Machine Learning Technique to Detect Malware</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="6079060" href="https://independent.academia.edu/IJRASETPublication">IJRASET Publication</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">Organizations have been threatened by malware for a long time, but timely detection of the virus remains a challenge. Malware may quickly damage the system by doing pointless tasks that burden it and prevent it from operating efficiently. There are two ways to detect malware: the traditional method that relies on the malware's signature and the behavior-based approach. The malware's behavior is characterized by the action it conducts when active in the machine, such as executing the operating system functions and downloading infected files from the internet. Based on how it behaves, the suggested algorithm finds the virus. The suggested model in this study is a hybrid of Support Vector Machine and Principle Component Analysis. For real Malware, our suggested model obtained an accuracy of 92.70% during validation, with 96% precision, 96.32% recall, and an f1score of .96.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Machine Learning Technique to Detect Malware","attachmentId":95620581,"attachmentType":"pdf","work_url":"https://www.academia.edu/92674519/A_Machine_Learning_Technique_to_Detect_Malware","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/92674519/A_Machine_Learning_Technique_to_Detect_Malware"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="83976212" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/83976212/A_Novel_Approach_for_Predicting_the_Malware_Attacks">A Novel Approach for Predicting the Malware Attacks</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="194342" href="https://veheretech.academia.edu/soumenkanrar">soumen kanrar</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Computer Applications, 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">Malware means malicious software. Detecting malware over a system is malware analysis. It consists of two parts static analysis and dynamic analysis. Static analysis includes analyzing a suspicious file and dynamic analysis means observing a file during its process time. In this paper, we have proposed a framework for malware analysis based on semi automated malware detection usually machine learning which is based on dynamic malware detection. The framework shows the quality of experience (QoE) to maintain the efficiency tradeoffs and uses the method of classification. The samples of malware also shows that the framework create a strong detection method.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Novel Approach for Predicting the Malware Attacks","attachmentId":89151197,"attachmentType":"pdf","work_url":"https://www.academia.edu/83976212/A_Novel_Approach_for_Predicting_the_Malware_Attacks","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/83976212/A_Novel_Approach_for_Predicting_the_Malware_Attacks"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="49932454" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/49932454/Determining_malicious_executable_distinguishing_attributes_and_low_complexity_detection">Determining malicious executable distinguishing attributes and low-complexity detection</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="195711943" href="https://independent.academia.edu/HassanKhan732">Hassan Khan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal in Computer Virology, 2011</p><p class="ds-related-work--abstract ds2-5-body-sm">Detection of rapidly evolving malware requires classification techniques that can effectively and efficiently detect zero-day attacks. Such detection is based on a robust model of benign behavior and deviations from that model are used to detect malicious behavior. In this paper we propose a low-complexity host-based technique that uses deviations in static file attributes to detect malicious executables. We first</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Determining malicious executable distinguishing attributes and low-complexity detection","attachmentId":68105931,"attachmentType":"pdf","work_url":"https://www.academia.edu/49932454/Determining_malicious_executable_distinguishing_attributes_and_low_complexity_detection","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/49932454/Determining_malicious_executable_distinguishing_attributes_and_low_complexity_detection"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":86294556,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":86294556,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_86294556" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="116887271" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/116887271/Malware_Detection_Framework_Using_PCA_Based_ANN">Malware Detection Framework Using PCA Based ANN</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="308184978" href="https://gtu-in.academia.edu/DrKhyatiRami">Dr. Khyati Rami</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Computing Science, Communication and Security, 2020</p><div class="ds-related-work--ctas"><button 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