How robust are malware detection models for Android smartphones against adversarial attacks?: Poster abstract

Abstract

Android-based smartphones and IoT devices have grown at an exponential rate in the last decade. Meanwhile, malicious applications have also increased dramatically, which threaten the Android ecosystem. The anti-malware community has proposed data mining based malware detection models which have shown encouraging results. However, these detection models are vulnerable to adversarial attacks. In this work, we first acted as an adversary and performed adversarial attacks on eight different malware detection models. We found all the eight detection models vulnerable to adversarial attacks and fooling rate of more than 90% was achieved against each of them. We also propose defence against these attacks by adversarial retraining and accomplish encouraging results to improve the overall robustness of malware detection models.