Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach
<|reference_start|>Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach: 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.<|reference_end|>
@article{etter2024evading,
    title={Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep
  Reinforcement Learning Approach},
    author={Brian Etter, James Lee Hu, Mohammedreza Ebrahimi, Weifeng Li, Xin Li,
  Hsinchun Chen},
    journal={arXiv preprint arXiv:2402.02600},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02600},
    primaryClass={cs.CR cs.AI cs.LG}
}