XGA-E: an explainability-enhanced graph neural network for network traffic anomaly detection
Abstract Graph neural network (GNN) have demonstrated excellent performance in network traffic anomaly detection research. However, existing GNN-based approaches often lack interpretability, and their detection performance remains to be improved. To address these challenges, we propose XGA-E, an int...
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| Autors principals: | , |
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| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
SpringerOpen
2026-01-01
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| Col·lecció: | Cybersecurity |
| Matèries: | |
| Accés en línia: | https://doi.org/10.1186/s42400-025-00487-x |
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