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.
Learning to configure computer networks with neural algorithmic reasoning,
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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.