NETWORK ATTACK DETECTION USING GRAPH NEURAL NETWORKS

Abstract

This paper presents a graph neural network (GNN)-based approach for network attack detection, emphasizing the representation of hosts and flows as heterogeneous graphs. By leveraging topological and relational dependencies, the proposed models—GraphSAGE, GAT, and temporal GNN—demonstrate superior adaptability and accuracy compared to traditional intrusion detection systems. Evaluations on CIC-IDS2017, UNSW-NB15, and real NetFlow data confirm that GNNs effectively capture multi-stage and evolving attack behaviors while maintaining robustness under dynamic network conditions.