Adaptive Two-Stage Online Learning for Service-Affecting Failure Detection in Mobile Core Networks

• URL: http://arxiv.org/abs/2607.18522v1
• Date: Mon, 20 Jul 2026 21:33:56 GMT
• Title: Adaptive Two-Stage Online Learning for Service-Affecting Failure Detection in Mobile Core Networks
• Abstract summary: This paper proposes a two-stage online learning framework for traffic-based failure detection in mobile core networks.<n>The framework operates fully online under a prequential evaluation protocol, enabling continuous adaptation with low computational overhead.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mobile network operators monitor aggregated traffic volumes to assess the operational health of core network infrastructure. Reliable failure detection is challenging due to strong temporal structure, non-stationarity, measurement artefacts, and extreme class imbalance, which limit static threshold-based monitoring. This paper proposes a two-stage online learning framework for traffic-based failure detection in mobile core networks. Stage I incrementally models normal traffic dynamics using lightweight regression with time-aware features. Stage II analyses prediction residuals together with contextual indicators to detect genuine service-affecting network failures. The framework operates fully online under a prequential evaluation protocol, enabling continuous adaptation with low computational overhead. Across linear and non-linear models, the proposed two-stage architecture achieves the best precision-recall trade-off, attaining the highest recall, F1-score, and AUC at acceptable false positive rates. These results demonstrate the importance of explicit residual decomposition for reliable failure detection in streaming mobile core network data.

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