Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks

@article{Caville2022AnomalEAS,
  title={Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks},
  author={Evan Caville and Wai Weng Lo and Siamak Layeghy and Marius Portmann},
  journal={Knowl. Based Syst.},
  year={2022},
  volume={258},
  pages={110030}
}
This paper investigates Graph Neural Networks (GNNs) application for self-supervised intrusion and anomaly detection in computer networks. GNNs are a deep learning approach for graph-based data that incorporate graph structures into learning to generalise graph representations and output embeddings. As tra ffi c flows in computer networks naturally exhibit a graph structure, GNNs are a suitable fit in this context. The majority of current implementations of GNN-based Network Intrusion Detection… 

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