ADVeRL-ELF: ADVersarial ELF Malware Generation using Reinforcement Learning
Akshara Ravi, Vivek Chaturvedi, Muhammad Shafique · 2025
Deep learning models are now pervasive in the malware detection domain owing to their high accuracy and performance efficiency. However, it is critical to analyze the robustness of these models by introducing adversarial attacks that can expose their vulnerabilities. Nevertheless, adversarial malware generation problem for Linux has not been well-investigated. In this work, we propose a novel reinforcement learning framework, ADVeRL-ELF to generate adversarial ELF malware by adding semantic NOPs within the executable region. Experimental results show that ADVeRL-ELF achieved an attack success rate of 59.5%. These adversarial malware can be leveraged to harden the Linux based malware detection systems.