Designing Adversarial Attack and Defence for Robust Android Malware Detection Models

The last decade witnessed an exponential rise of Android smartphones and malware attacks on them. Researchers have investigated and proposed many promising malware detection models based on machine learning. However, these malware detection models are susceptible to adversarial attacks which threaten the Android security ecosystem. In this article, we propose to develop malware detection models which are more robust against adversarial attacks. We first designed an i-bit adversarial attack policy which achieved an average fooling rate of 51% with maximum ten modifications across twelve different malware detection models. Later we also propose an adversarial defence mechanism which enhanced the robustness of the malware detection models by reducing the fooling rate to one-third against the same adversarial attack.