Sample-Efficient Misconfiguration Classification for Network Resilience in Wireless Communications
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913144450842624 |
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| author | Hao, Xin Zhang, Chenhan Piccardi, Massimo Chemalamarri, Vijaya Durga Jiang, Qiwen Ni, Wei Owen, Raymond |
| author_facet | Hao, Xin Zhang, Chenhan Piccardi, Massimo Chemalamarri, Vijaya Durga Jiang, Qiwen Ni, Wei Owen, Raymond |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19303 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sample-Efficient Misconfiguration Classification for Network Resilience in Wireless Communications Hao, Xin Zhang, Chenhan Piccardi, Massimo Chemalamarri, Vijaya Durga Jiang, Qiwen Ni, Wei Owen, Raymond Networking and Internet Architecture 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. |
| title | Sample-Efficient Misconfiguration Classification for Network Resilience in Wireless Communications |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2605.19303 |