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.
@article{arxiv.2402.02600,
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},
journal= {arXiv preprint arXiv:2402.02600},
year = {2024}
}