Projects GNN embeddings of IoT traffic onto interpretable manifolds to achieve 0.83 F1-score intrusion detection and reveal concept drift.
GNN-Based Network Traffic Analysis for the Detection of Sequential Attacks in IoT.Electronics, 13(12):2274, June 2024
2 Pith papers cite this work, alongside 33 external citations. Polarity classification is still indexing.
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GraphSAGE achieves 94.2% detection rate, 0.955 AUC, and 1.4s response time for cyberattacks on drone systems in an emulation study, outperforming GCN and GAT.
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Interpreting Manifolds and Graph Neural Embeddings from Internet of Things Traffic Flows
Projects GNN embeddings of IoT traffic onto interpretable manifolds to achieve 0.83 F1-score intrusion detection and reveal concept drift.
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GraphSAGE achieves 94.2% detection rate, 0.955 AUC, and 1.4s response time for cyberattacks on drone systems in an emulation study, outperforming GCN and GAT.