RGDroid: Detecting Android Malware with Graph Convolutional Networks against Structural Attacks
Yakang Li, Yikun Hu, Yizhuo Wang, Yituo He, Haining Lu, Dawu Gu · 2023
The rapid growth of Android malware calls for anti-malware systems to detect malware automatically. Detecting malware effectively is a non-trivial problem due to the high overlap in behaviors between malware and benign apps. Most existing automated Android malware detection methods use statistic features extracted from apps or graphs generated from method calls to identify malware. However, the methods that only use statistic features lead to false positives due to ignoring program semantics. Existing graph-based approaches suffer scalability problems due to the heavy-weight program analysis and time-consuming graph matching. In addition, graph-based approaches could be evaded by modifying dependencies among method calls. As a result, crafted malicious apps resemble the benign ones.In this paper, we propose a novel deep learning-based detection system, named RGDroid, which is capable of detecting malware under graph structural attacks. It combines API information extracted from Android document and learns behavior features from function call graph by graph neural network. Specifically, to defend against graph adversarial attacks, RGDroid reduces the connectivity of different functional parts to mitigate the effect of structural modifications on the final graph embedding. To comprehensively evaluate the robustness of RGDroid, we implement four influential graph adversarial attacks to simulate current capabilities and knowledge of Android malware attackers. The attack success rate (ASR) of two state-of-the-art detection systems (i.e., MaMaDroid, MalScan) is above 70.0% while the ASR of RGDroid under the four graph attacks is below 6.1%.