Deep Learning-Based Detection of Android Malware using Graph Convolutional Networks (GCN)

Abstract

The study is centered around identifying Android malware using deep learning methods through Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs). With Android being widely used worldwide ensuring the security of released applications poses a challenge. Conventional malware detection techniques, like dynamic analysis have limitations in recognizing new malware types leading to a shift towards machine learning and deep learning solutions. The research introduces a malware detection system that employs GNNs particularly focusing on GCNs to analyze the relationships within an applications code by transforming APK files into graph formats. The system follows stages including data gathering, feature extraction, graph construction, model training and implementation. By concentrating on function call graphs the system proves effective in identifying software surpassing traditional machine learning methods in terms of accuracy, precision, recall and F1 score. The GCN based model shows enhancements over approaches with an accuracy rate of 95% compared to 89%, for traditional machine learning models. This progress highlights the potential of learning techniques in bolstering Android security.

The system excels not in identifying software but also proves versatile, for different uses like screening apps, in stores and functioning as a standalone antivirus program.