MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers

TL;DR

This paper introduces a reinforcement learning framework for black-box adversarial attacks on malware classifiers, achieving high evasion rates and providing insights into attack transferability.

Contribution

It presents a novel RL-based black-box attack framework that models malware evasion as a multi-armed bandit problem, improving attack success rates against commercial antivirus systems.

Findings

Achieves 74-97% evasion rate on ML malware detectors.

Attains 32-48% evasion rate on commercial antivirus in black-box settings.

Higher transferability among ML classifiers than between ML and commercial AVs.

Abstract

Modern commercial antivirus systems increasingly rely on machine learning to keep up with the rampant inflation of new malware. However, it is well-known that machine learning models are vulnerable to adversarial examples (AEs). Previous works have shown that ML malware classifiers are fragile to the white-box adversarial attacks. However, ML models used in commercial antivirus products are usually not available to attackers and only return hard classification labels. Therefore, it is more practical to evaluate the robustness of ML models and real-world AVs in a pure black-box manner. We propose a black-box Reinforcement Learning (RL) based framework to generate AEs for PE malware classifiers and AV engines. It regards the adversarial attack problem as a multi-armed bandit problem, which finds an optimal balance between exploiting the successful patterns and exploring more varieties. Compared to other frameworks, our improvements lie in three points. 1) Limiting the exploration space by modeling the generation process as a stateless process to avoid combination explosions. 2) Due to the critical role of payload in AE generation, we design to reuse the successful payload in modeling. 3) Minimizing the changes on AE samples to correctly assign the rewards in RL learning. It also helps identify the root cause of evasions. As a result, our framework has much higher black-box evasion rates than other off-the-shelf frameworks. Results show it has over 74\%--97\% evasion rate for two state-of-the-art ML detectors and over 32\%--48\% evasion rate for commercial AVs in a pure black-box setting. We also demonstrate that the transferability of adversarial attacks among ML-based classifiers is higher than the attack transferability between purely ML-based and commercial AVs.