Integrated Feature Extraction Approach Towards Detection of Polymorphic Malware In Executable Files

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dc.contributor.authorMasabo, Emmanuel
dc.contributor.authorKaawaase, Kyanda Swaib
dc.contributor.authorSansa-Otim, Julianne
dc.contributor.authorHanyurwimfura, Damien
dc.date.accessioned2022-05-02T20:47:10Z
dc.date.available2022-05-02T20:47:10Z
dc.date.issued2016
dc.description.abstractSome malware are sophisticated with polymorphic techniques such as self-mutation and emulation based analysis evasion. Most anti-malware techniques are overwhelmed by the polymorphic malware threats that self-mutate with different variants at every attack. This research aims to contribute to the detection of malicious codes, especially polymorphic malware by utilizing advanced static and advanced dynamic analyses for extraction of more informative key features of a malware through code analysis, memory analysis and behavioral analysis. Correlation based feature selection algorithm will be used to transform features; i.e. filtering and selecting optimal and relevant features. A machine learning technique called K-Nearest Neighbor (K-NN) will be used for classification and detection of polymorphic malware. Evaluation of results will be based on the following measurement metrics—True Positive Rate (TPR), False Positive Rate (FPR) and the overall detection accuracy of experimentsen_US
dc.identifier.citationMasabo, E., Sansa-otim, J., & Hanyurwimfura, D. (2016). Integrated Feature Extraction Approach Towards Detection of Polymorphic Malware In Executable Files. International Journal of Computer Science and Security (IJCSS, 11(2), 25-33.en_US
dc.identifier.urihttps://repository.ruforum.org/system/tdf/Masabo%202017a.pdf?file=1&type=node&id=37283&force=
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/3155
dc.language.isoenen_US
dc.publisherInternational Journal of Computer Science and Security (IJCSSen_US
dc.subjectMalware Detectionen_US
dc.subjectStatic Analysisen_US
dc.subjectDynamic Analysisen_US
dc.subjectPolymorphic Malwareen_US
dc.subjectMachine Learningen_US
dc.titleIntegrated Feature Extraction Approach Towards Detection of Polymorphic Malware In Executable Filesen_US
dc.typeArticleen_US
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