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Title:
Оbfuscated Мalware Detection Model
Creator:
Jamgharyan, Timur V. ; Iskandaryan, Vaghashak S. ; Khemchyan, Artak A.
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Uncontrolled Keywords:
Obfuscation ; Data flow ; Convolutional neural network ; Machine learning
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Abstract:
The paper presents the research results on the detection of obfuscated malware using a method based on mean shift. The research aimed to train neural networks included in the intrusion detection system to detect obfuscated malware. Detection of obfuscated malware using deterministic obfuscators is also discussed. Software solutions Dotfuscator CE, Net Reactor, and Pro Guardwere used as deterministic obfuscators. Athena, abc, cheeba, dyre, december_3, engrat, surtr, stasi, otario, dm, v-sign, tequila, flip, grum, mimikatz were used as test malware. The results were verified using the IDA Pro tool and various intrusion detection systems. Process modeling was carried out in the Hyper-V virtual environment.