Feb. 6, 2024, 5:10 a.m. | Brian Etter James Lee Hu Mohammedreza Ebrahimi Weifeng Li Xin Li Hsinchun Chen

cs.CR updates on arXiv.org arxiv.org

Adversarial Malware Generation (AMG), the generation of adversarial malware variants to strengthen Deep Learning (DL)-based malware detectors has emerged as a crucial tool in the development of proactive cyberdefense. However, the majority of extant works offer subtle perturbations or additions to executable files and do not explore full-file obfuscation. In this study, we show that an open-source encryption tool coupled with a Reinforcement Learning (RL) framework can successfully obfuscate malware to evade state-of-the-art malware detection engines and outperform techniques that …

adversarial cs.ai cs.cr cs.lg cyberdefense deep learning development file files malware obfuscation offer proactive tool

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