MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks

ArXi:2312.06423v3 Announce Type: replace-cross Machine learning (ML) has gained significant adoption in Android malware detection to address the escalating threats posed by the rapid proliferation of malware attacks. However, recent studies have revealed the inherent vulnerabilities of ML-based detection systems to evasion attacks. While efforts have been made to address this critical issue, many of the existing defensive methods encounter challenges such as lower effectiveness or reduced generalization capabilities. In this paper, we