Malware Detection System Based on Static and Dynamic Analysis Using Machine Learning

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dc.contributor.authorNafiiev, Alan
dc.contributor.authorRodionov, Andrii
dc.date.accessioned2023-11-23T08:19:41Z
dc.date.available2023-11-23T08:19:41Z
dc.date.issued2023
dc.description.abstractCyber wars and cyber attacks are an urgent problem in the global digital environment. Based on existing popular detection methods, malware authors are creating ever more advanced and sophisticated malware. Therefore, this study aims to create a malware analysis system that uses both dynamic and static analysis. Our system is based on a machine learning method - support vector machine. The set of data used was collected from various Internet sources. It consists of 257 executable files in .exe format, 178 of which are malicious and 79 are benign. We use 5 different types of data representation: binary information, trace instructions, control flow graph, information obtained from the dynamic operation of the file, and file metadata. Then, using multiple kernel learning, we combine all data views and create one summative machine learning model.uk
dc.format.pagerangePp. 97-104uk
dc.identifier.citationNafiiev, A. Malware Detection System Based on Static and Dynamic Analysis Using Machine Learning / Alan Nafiiev, Andrii Rodionov // Theoretical and Applied Cybersecurity : scientific journal. – 2023. – Vol. 5, Iss. 2. – Pp. 97–104. – Bibliogr. 11 ref.uk
dc.identifier.doihttps://doi.org/10.20535/tacs.2664-29132023.2.277959
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/62410
dc.language.isoukuk
dc.publisherIgor Sikorsky Kyiv Polytechnic Instituteuk
dc.publisher.placeKyivuk
dc.relation.ispartofTheoretical and Applied Cybersecurity: scientific journal, Vol. 5, No. 2uk
dc.subjectmalware detectionuk
dc.subjectmalware dynamic analysisuk
dc.subjectfeature selectionuk
dc.subjectmultiple kernel learninguk
dc.subject.udc004.056uk
dc.titleMalware Detection System Based on Static and Dynamic Analysis Using Machine Learninguk
dc.typeArticleuk

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Theoretical and Applied Cybersecurity: scientific journal, Vol. 5, No. 2