EtaGATv2, an edge-type-aware graph attention network, classifies protocol misconfigurations in wireless networks at state-of-the-art levels using 50% of the training samples by addressing non-uniform symptom propagation and protocol-specific features.
Graph attention networks,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
fields
cs.NI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
GSID applies an adaptive configuration encoder and inconsistency dynamic attention on bipartite graphs to detect protocol configuration anomalies, reporting threefold F1 improvement and 23.2% accuracy gain over baselines.
citing papers explorer
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Sample-Efficient Misconfiguration Classification for Network Resilience in Wireless Communications
EtaGATv2, an edge-type-aware graph attention network, classifies protocol misconfigurations in wireless networks at state-of-the-art levels using 50% of the training samples by addressing non-uniform symptom propagation and protocol-specific features.
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Enhancing Network Resilience via Graph-Based Anomaly Detection in Sovereign Functions
GSID applies an adaptive configuration encoder and inconsistency dynamic attention on bipartite graphs to detect protocol configuration anomalies, reporting threefold F1 improvement and 23.2% accuracy gain over baselines.