Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors

• Published in: arXiv.org
• Main Authors: Sun, Ruoxi, Xue, Minhui, Tyson, Gareth, Tian Dong, Li, Shaofeng, Wang, Shuo, Zhu, Haojin, Camtepe, Seyit, Nepal, Surya
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 27.11.2023
• Subjects: Detectors; Machine learning; Malware; Sensors; State-of-the-art reviews
• ISSN: 2331-8422

Numerous open-source and commercial malware detectors are available. However, their efficacy is threatened by new adversarial attacks, whereby malware attempts to evade detection, e.g., by performing feature-space manipulation. In this work, we propose an explainability-guided and model-agnostic testing framework for robustness of malware detectors when confronted with adversarial attacks. The framework introduces the concept of Accrued Malicious Magnitude (AMM) to identify which malware features could be manipulated to maximize the likelihood of evading detection. We then use this framework to test several state-of-the-art malware detectors' abilities to detect manipulated malware. We find that (i) commercial antivirus engines are vulnerable to AMM-guided test cases; (ii) the ability of a manipulated malware generated using one detector to evade detection by another detector (i.e., transferability) depends on the overlap of features with large AMM values between the different detectors; and (iii) AMM values effectively measure the fragility of features (i.e., capability of feature-space manipulation to flip the prediction results) and explain the robustness of malware detectors facing evasion attacks. Our findings shed light on the limitations of current malware detectors, as well as how they can be improved.