Android Malware Detection Based on Behavioral-Level Features with Graph Convolutional Networks
Qingling Xu, Dawei Zhao, Shumian Yang, Lijuan Xu, Xin Li
Abstract
Android malware detection is a critical research field due to the increasing prevalence of mobile devices and apps. Improved methods are necessary to address Android apps’ complexity and malware’s elusive nature. We propose an approach for Android malware detection based on Graph Convolutional Networks (GCNs). Our method focuses on learning the behavioral-level features of Android applications using the call graph extracted from the application’s Dex file. Combining the call graph with sensitive permissions and opcodes creates a new subgraph representing the application’s runtime behavior. Subsequently, we propose an enhanced detection model utilizing graph convolutional networks (GCNs) for Android malware detection. The experimental results demonstrate our proposed method’s high precision and accuracy in detecting malicious code. With a precision of 98.89% and an F1-score of 98.22%, our approach effectively identifies and classifies Android malicious code.