Communication Dans Un Congrès Année : 2025

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.

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Dates et versions

hal-04929581 , version 1 (04-02-2025)

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Copyright (Tous droits réservés)

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  • HAL Id : hal-04929581 , version 1

Citer

Killian Cressant, Pedro B Velloso, Stefano Secci. GNN graph structures in network anomaly detection. Network Operations and Management Symposium, IEEE; IFIP, May 2025, Honolulu (Hawaii), United States. ⟨hal-04929581⟩
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