Enhancing the insertion of NOP instructions to obfuscate malware via deep reinforcement learning

•It explores the vulnerability of classifiers against the dead code insertion technique.•It poposes a reinforcement learning framework to bypass malware classifers.•A Q-network selects the optimal positions to which insert the NOP instructions.•Using a time-distributed layer to determine the optimal...

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Bibliographic Details
Published in:	Computers & security Vol. 113; p. 102543
Main Authors:	Gibert, Daniel, Fredrikson, Matt, Mateu, Carles, Planes, Jordi, Le, Quan
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier Ltd 01.02.2022 Elsevier Sequoia S.A
Subjects:	Artificial neural networks Assembly language source code Classification Classifiers Deep learning Deep Q-Network Deployment Evasion Experiments Families & family life Insertion Instructions Machine learning Malware Malware classification Methodological problems Mutation Networks Neural networks Obfuscation Reinforcement Reinforcement learning Robustness Obfuscation Deep Q-Network Malware classification Assembly language source code Reinforcement learning
ISSN:	0167-4048, 1872-6208
Online Access:	Get full text
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