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

Summary

Researchers propose ADD, a plug-in defense framework that improves the adversarial robustness of ML-based Android malware detection (AMD) methods against problem space attacks, which generate real adversarial malware rather than only adversarial feature vectors. The authors report that ADD performs well against the evaluated state-of-the-art problem space attacks across multiple ML-based AMD methods. They also state that ADD enhances the adversarial robustness of real-world antivirus solutions.

Mitigation

ADD, a plug-in adversarial Android malware defense framework that enhances the adversarial robustness of ML-based AMD methods against problem space attacks.

Topics

• Adversarial machine learning

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