Sample-Efficient Misconfiguration Classification for Network Resilience in Wireless Communications

2026-05-18

AI Summary

This work addresses network outages in wireless systems caused by inconsistent protocol configurations under dynamic topologies. The authors propose EtaGATv2, an algorithm that introduces edge-type awareness into graph attention networks for the first time, enabling effective modeling of behavioral discrepancies among heterogeneous routing protocols and non-uniform symptom propagation patterns. By leveraging a dynamic attention mechanism, EtaGATv2 captures critical diagnostic paths and protocol-specific features, achieving state-of-the-art performance on real-world complex topologies with only 50% of the training samples required by existing methods. This approach substantially improves both sample efficiency and accuracy in misconfiguration classification, offering a significant advance over current solutions.

Abstract

As modern wireless communication networks grow increasingly complex, network outages driven by the inconsistency between dynamic topologies and protocol configurations have become a critical concern. To solve this issue, we mathematically formulate a protocol misconfiguration classification problem as a graph-based learning task and solve it with our proposed EtaGATv2 algorithm, an edge-type-aware graph attention network with dynamic attention. EtaGATv2 addresses two critical challenges: i) it captures non-uniform symptom propagation for protocol misconfiguration classification tasks, where certain network paths and nodes become critical for diagnosis, and ii) it extracts protocol-specific features from heterogeneous routing protocols with distinct message-passing behaviors by utilizing edge-type-aware transformations. Experiments across diverse and real-world topologies demonstrate that EtaGATv2 reaches state-of-the-art performance with 50% of the training samples, making it particularly suitable for networks with dynamic topologies and limited negative-labeled data.

Problem

• misconfiguration classification
• network resilience
• wireless communications
• dynamic topologies
• protocol configuration

Innovation

• graph attention network
• edge-type-aware
• protocol misconfiguration
• sample-efficient learning
• network resilience