GNN graph structures in network anomaly detection
Structure de graph pour GNN dans les réseaux en détection d'anomalies
Résumé
The rise of xG networks has brought unprecedented capabilities in wireless communication, but the complexity of 5G and beyond 5G networks introduces challenges in ensuring reliability and performance. In this article, we propose a new approach to network anomaly detection by leveraging Graph Neural Networks (GNNs) and graph structure learning. GNNs are well-suited for capturing relationships in graph-structured data, making them an effective tool for detecting complex patterns in network behavior. We introduce a novel graph structure extracting feature semantics and demonstrate the effectiveness of GNNs in network anomaly detection. We show we can improve the accuracy up to 4% thanks to the proposed graph structure. The source code is available at https://github.com/Killiancressant/graph4 anomaly detection.