Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach

Brian Etter, James Lee Hu, Mohammedreza Ebrahimi, Weifeng Li, Xin Li +1 more
2/4/2024
cs.CRcs.AIcs.LG

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Abstract

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 use advanced modification methods. Our results show that the proposed method improves the evasion rate from 27%-49% compared to widely-used state-of-the-art reinforcement learning-based methods.

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Cite this paper

@article{etter2024evading,
  title  = {Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach},
  author = {Brian Etter and James Lee Hu and Mohammedreza Ebrahimi and Weifeng Li and Xin Li and Hsinchun Chen},
  year   = {2024},
  eprint = {2402.02600},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.02600v1}
}

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