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

Understand

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.