Defending Against Adversarial Malware Attacks on ML-Based Android Malware Detection Methods

Android malware threatens user privacy and data, so researchers use machine learning to detect it, but attackers can craft adversarial malware (malware modified to fool detection systems) that bypasses these defenses. This paper proposes ADD, a defense framework that works as a plug-in to make ML-based malware detection more robust against realistic adversarial attacks, and tests show it effectively protects multiple detection methods and real antivirus solutions.