发表机构
Vodacom Group Limited（沃达康集团有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对移动核心网络中基于流量的故障检测难题，提出两阶段在线学习框架。第一阶段用带时间感知特征的轻量级回归建模正常流量动态，第二阶段结合上下文指标分析预测残差。该框架在线运行，在多种模型中实现最佳精确率-召回率权衡，凸显残差分解对故障检测的重要性。
AI 中文摘要
移动网络运营商通过监控聚合流量来评估核心网络基础设施的运行状况。由于存在强烈的时间结构、非平稳性、测量伪像和极端类不平衡，可靠的故障检测具有挑战性，这限制了基于静态阈值的监控。本文提出了一种用于移动核心网络中基于流量的故障检测的两阶段在线学习框架。第一阶段使用具有时间感知特征的轻量级回归逐步对正常流量动态进行建模。第二阶段结合上下文指标分析预测残差，以检测真正影响服务的网络故障。该框架在序贯评估协议下完全在线运行，能够以低计算开销进行持续自适应。在各种线性和非线性模型中，所提出的两阶段架构实现了最佳的精确率-召回率权衡，在可接受的误报率下获得了最高的召回率、F1分数和AUC。这些结果证明了显式残差分解对于移动核心网络流数据中可靠故障检测的重要性。
英文摘要
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.
Comments8 pages, 3 figures
