An Efficient Adversarial Attack on FCG-Based Android Malware Detection Systems

Abstract

Function Call Graph (FCG) based Android malware detectors can achieve satisfactory detection performance but are vulnerable to adversarial examples (AEs). Existing adversarial attacks generate AEs separately and specifically for different APKs (termed as APK-specific attacks), resulting in significant computational overhead and limited attack efficiency. In this paper, we propose an APK-Agnostic Adversarial Attack Method (termed as A4M) for FCG-based Android malware detection, enabling the deployment of large-scale malware adversarial examples. Meanwhile, this perturbation can also greatly accelerate existing APK-specific attacks. We conduct extensive experiments to evaluate the effectiveness and efficiency of A4M. A4M achieves an average attack success rate (ASR) of 85.17% on 7 target detectors (built with MAMADroid, APIGraph and GNN), significantly surpassing the state-of-the- art attack MalPatch by 28.17%. Experiments also demonstrate A4M can markedly accelerate the APK-specific attacks HIV_CW, HIV_JSMA and DQN, reducing about 88 queries per adversarial example.