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

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.

References in corpus (18)

• On Evaluating Adversarial Robustness
• Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
• Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning
• EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models
• Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
• Adversarial Samples on Android Malware Detection Systems for IoT Systems
• Android Malware Detection using Deep Learning on API Method Sequences
• Generation & Evaluation of Adversarial Examples for Malware Obfuscation
• Adversarial Detection of Flash Malware: Limitations and Open Issues
• COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection
• HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples
• Attack and Defense of Dynamic Analysis-Based, Adversarial Neural Malware Classification Models
• Enhancing Robustness of Deep Neural Networks Against Adversarial Malware Samples: Principles, Framework, and AICS'2019 Challenge
• Easy to Fool? Testing the Anti-evasion Capabilities of PDF Malware Scanners
• On Visual Hallmarks of Robustness to Adversarial Malware
• Examining Adversarial Learning against Graph-based IoT Malware Detection Systems
• Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions
• Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions

Cited by in corpus (3)

• Creating Valid Adversarial Examples of Malware
• A Comparison of Adversarial Learning Techniques for Malware Detection
• Dissecting Malware in the Wild