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

• Raul Suzuki Borges UFV
• Rodrigo Moreira UFV
• Pedro Henrique A. Damaso de Melo UFV
• Larissa F. Rodrigues Moreira UFV
• Flávio de Oliveira Silva Uminho

DOI: https://doi.org/10.5753/sbrc.2026.18039

Resumo

Detecting Internet routing instability is a critical yet challenging task, particularly when relying solely on endpoint active measurements. This study introduces TRACE, a Machine Learning (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 effectiveness in detecting routing changes on the Internet.

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