MPass: Bypassing Learning-Based Static Malware Detectors

MPass: Bypassing Learning-Based Static Malware Detectors

연구 분야: Strategies

학회: DAC '23: Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

Machine learning (ML) based static malware detectors are widely deployed, but vulnerable to adversarial attacks. Unlike images or texts, tiny modifications to malware samples would significantly compromise their functionality. Consequently, existing attacks against images or texts will be significantly restricted when being deployed on malware detectors. In this work, we propose a hard-label black-box attack MPass against ML-based detectors. MPass employs a problemspace explainability method to locate critical positions of malware, applies adversarial modifications to such positions, and utilizes a runtime recovery technique to preserve the functionality. Experiments show MPass outperforms existing solutions and bypasses both state-of-the-art offline models and commercial ML-based antivirus products.