Sample-Efficient Misconfiguration Classification for Network Resilience in Wireless Communications

TL;DR

This paper introduces EtaGATv2, a graph attention network that efficiently classifies network misconfigurations in wireless networks, achieving state-of-the-art results with limited training data.

Contribution

The paper presents EtaGATv2, a novel edge-type-aware graph attention network designed for protocol misconfiguration classification in dynamic wireless networks.

Findings

EtaGATv2 achieves state-of-the-art performance with only 50% of training samples.

It effectively captures non-uniform symptom propagation in network diagnosis.

The method handles heterogeneous routing protocols with distinct message-passing behaviors.

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.