NT-GNN: Network Traffic Graph for 5G Mobile IoT Android Malware Detection

Abstract

IoT Android application is the most common implementation system in the mobile ecosystem. As assaults have increased over time, malware attacks will likely happen on 5G mobile IoT Android applications. The huge threat posed by malware to communication systems security has made it one of the main focuses of information security research. Therefore, this paper proposes a new graph neural network model based on a network traffic graph for Android malware detection (NT-GNN). While some current malware detection systems use network traffic data for detection, they ignore the complex structural relationships of network traffic, focusing exclusively on network traffic between pairs of endpoints. Additionally, our suggested network traffic graph neural network model (NT-GNN) considers the graph node and edge aspects, capturing the connection between various traffic flows and individual traffic attributes. We first extract the network traffic graph and then detect it using a novel graph neural network architecture. Finally, we experimented with the proposed NT-GNN model on the well-known Android malware CICAndMal2017 and AAGM datasets and achieved 97% accuracy. The results reflect the sophisticated nature of our methodology. Furthermore, we want to provide a new method for malicious code detection.