Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN
Recent advancements in the Internet of Things (IoT) emphasize the urgent need for advanced network security, as IoT networks feature dynamic topologies, imbalanced traffic, and complex attack patterns. Unlike general IT networks, IoT environments exhibit extreme heterogeneity and sparse topologies. Traditional GNN-based intrusion detection methods often struggle to efficiently model node and edge features or capture fine-grained anomalies in such settings. To address this, we propose SKGFusionKAN, a novel IoT-tailored approach enhancing GraphSAGE with a multi-scale selective kernel attention mechanism. This enables adaptive extraction of node and edge features under diverse traffic condit...
| Authors | Long Zhao, Shixun Ji, Bin Cheng et al. |
|---|---|
| Primary category | cs.CR |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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