RGDroid: Detecting Android Malware with Graph Convolutional Networks against Structural Attacks

Abstract

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 timeconsuming 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.