TRACE: Traceroute-based Internet Route change Analysis with Ensemble Learning

• URL: http://arxiv.org/abs/2604.02361v1
• Date: Sat, 21 Mar 2026 13:02:16 GMT
• Title: TRACE: Traceroute-based Internet Route change Analysis with Ensemble Learning
• Abstract summary: This study introduces TRACE, a MachineLearningpipeline designed to identify route changes using only traceroute latency data.<n>We propose a robust feature engineering strategy that captures temporal dynamics using rolling statistics and aggregated context patterns.
• Score: 0.046603287532620725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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