A Deep Learning Framework Based on GCN Model for Android Malware Detection

Abstract

Nowadays, Android malwares are increasingly significantly producing major security issues. The complexity and increase of malware threats have made automated malware detection research an important component of network security. Traditional malware detection methods include manual examination of every malware file present in the application, which consumes a significant number of human resources (on the basis of both storage and time). Additionally, malware developers have created methods like code obfuscation to get beyond antivirus companies' conventional signature-based detection methods. Deep learning (DL) approaches for malware detection are now being used to resolve this issue. In this study, Performance comparisons are made amongst GCN (Graph Convolutional Network) models for Android malware detection. Using graph-based representations of malware of the Android DEX file, a GCN-based model is suggested to detect Android malware. GCN extracts the necessary features from the images of malware. The static approach is used to extract the essential features. Then, these features train GCN to detect malware. We presented a GCNs latest version for modeling more advanced graphical semantics. It automatically discovers and understands semantic and ordered patterns based on the previous stage's vectors, without requiring additional sophisticated or expert features. The proposed method outperformed the compared models in every performance metric, achieving an accuracy of 99.69% compared to other approaches.