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 …

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