Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks. (arXiv:2207.06819v1 [cs.LG])

This paper investigates Graph Neural Networks (GNNs) application for
self-supervised network intrusion and anomaly detection. GNNs are a deep
learning approach for graph-based data that incorporate graph structures into
learning to generalise graph representations and output embeddings. As network
flows are naturally graph-based, GNNs are a suitable fit for analysing and
learning network behaviour. The majority of current implementations of
GNN-based Network Intrusion Detection Systems (NIDSs) rely heavily on labelled
network traffic which can not only restrict the amount and …

detection intrusion intrusion detection intrusion detection system lg network networks neural networks system