Robust Android Malware Detection System against Adversarial Attacks using Q-Learning

no code implementations • 27 Jan 2021 • Hemant Rathore, Sanjay K. Sahay, Piyush Nikam, Mohit Sewak

Finally, we propose an adversarial defense strategy that reduces the average fooling rate by threefold to 15. 22% against a single policy attack, thereby increasing the robustness of the detection models i. e. the proposed model can effectively detect variants (metamorphic) of malware.

Adversarial Defense Android Malware Detection +5