Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting

Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting

연구 분야: Strategies

학회: CCS '23: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security

The widespread adoption of the Android operating system has made malicious Android applications an appealing target for attackers. Machine learning-based (ML-based) Android malware detection (AMD) methods are crucial in addressing this problem; however, their vulnerability to adversarial examples raises concerns. Current attacks against ML-based AMD methods demonstrate remarkable performance but rely on strong assumptions that may not be realistic in real-world scenarios, e.g., the knowledge requirements about feature space, model parameters, and training dataset. To address this limitation, we introduce AdvDroidZero, an efficient query-based attack framework against ML-based AMD methods that operates under the zero knowledge setting. Our extensive evaluation shows that AdvDroidZero is effective against various mainstream ML-based AMD methods, in particular, state-of-the-art such methods and real-world antivirus solutions.