Evading Static and Dynamic Android Malware Detection Mechanisms

Abstract

With the widespread usage of Android mobile devices, malware developers are increasingly targeting Android applications for carrying out their malicious activities. Despite employing powerful malware detection mechanisms, an adversary can evade the threat detection model by launching intelligent malware with fine-grained feature perturbations. Since machine learning is widely adopted in malware detection owing to its automatic threat prediction and detection capabilities, attackers are nowadays targeting the vulnerability of machine learning models for malicious activities. In this work, we demonstrate how an adversary can evade various machine learning based static and dynamic Android malware detection mechanisms. To the best of our knowledge, this is the first work that discusses adversarial evasion in both static and dynamic machine learning based malware detection mechanisms.