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.
@article{arxiv.2309.01866,
title = {Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting},
author = {Ping He and Yifan Xia and Xuhong Zhang and Shouling Ji},
journal= {arXiv preprint arXiv:2309.01866},
year = {2023}
}
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To Appear in the ACM Conference on Computer and Communications Security, November, 2023