GBKPA and AuxShield: Addressing adversarial robustness and transferability in android malware detection

Abstract

Android stands as the predominant operating system within the mobile ecosystem. Users can download applications from official sources like Google Play Store and other third-party platforms. However, malicious actors can attempt to compromise user device integrity through malicious applications. Traditionally, signatures, rules, and other methods have been employed to detect malware attacks and protect device integrity. However, the growing number and complexity of malicious applications have prompted the exploration of newer techniques like machine learning (ML) and deep learning (DL). Many recent studies have demonstrated promising results in detecting malicious applications using ML and DL solutions. However, research in other fields, such as computer vision, has shown that ML and DL solutions are vulnerable to targeted adversarial attacks. Malicious actors can develop malicious adversarial applications that can bypass ML and DL based anti-viruses. The study of adversarial techniques related to malware detection has now captured the security community’s attention. In this work, we utilise android permissions and intents to construct 28 distinct malware detection models using 14 classification algorithms. Later, we introduce a novel targeted false-negative evasion attack, Gradient Based K Perturbation Attack (GBKPA), designed for grey-box knowledge scenarios to assess the robustness of these models. The GBKPA attempts to craft malicious adversarial samples by making minimal perturbations without violating the syntactic and functional structure of the application. GBKPA achieved an average fooling rate (FR) of 77 % with only five perturbations across the 28 detection models. Additionally, we identified the most vulnerable android permissions and intents that malicious actors can exploit for evasion attacks. Furthermore, we analyse the transferability of adversarial samples across different classes of models and provide explanations for the same. Finally, we proposed AuxShield defence mechanism to develop robust detection models. AuxShield reduced the average FR to 3.25 % against 28 detection models. Our findings underscore the need to understand the causation of adversarial samples, their transferability, and robust defence strategies before deploying ML and DL solutions in the real world.