用追踪数据检测网络路由变化，无需控制面信息。
TRACE: Traceroute-based Internet Route change Analysis with Ensemble Learning
- 基于滚动统计和上下文聚合提取时序特征
- 集成学习模型在罕见事件上实现高F1分数
- 适合网络运维与安全监控人员使用
检测互联网路由不稳定性是一项关键但具有挑战性的任务，尤其当仅依赖端点主动测量时。本研究提出TRACE，一种仅使用traceroute延迟数据识别路由变更的机器学习管道，确保独立于控制平面信息。我们设计了一种稳健的特征工程策略，通过滚动统计和聚合上下文模式捕捉时间动态。该架构采用堆叠集成的梯度提升决策树，并由超参数优化的元学习器精炼。通过严格校准决策阈值以应对罕见路由事件的固有类别不平衡问题，TRACE在性能上显著优于传统基线模型，在真实互联网环境中有效检测路由变化。
原文摘要 · Abstract (English)
Detecting Internet routing instability is a critical yet challenging task, particularly when relying solely on endpoint active measurements. This study introduces TRACE, a MachineLearning (ML)pipeline designed to identify route changes using only traceroute latency data, thereby ensuring independence from control plane information. We propose a robust feature engineering strategy that captures temporal dynamics using rolling statistics and aggregated context patterns. The architecture leverages a stacked ensemble of Gradient Boosted Decision Trees refined by a hyperparameter-optimized meta-learner. By strictly calibrating decision thresholds to address the inherent class imbalance of rare routing events, TRACE achieves a superior F1-score performance, significantly outperforming traditional baseline models and demonstrating strong effective ness in detecting routing changes on the Internet.
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