Android恶意软件
Android(操作系统)
计算机科学
恶意软件
计算机安全
对抗制
机器学习
人工智能
操作系统
作者
Pinjia He,Yifan Xia,Xuhong Zhang,Shouling Ji
出处
期刊:Cornell University - arXiv
日期:2023-09-04
标识
DOI:10.48550/arxiv.2309.01866
摘要
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.
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