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.
@article{arxiv.2604.02361,
title = {TRACE: Traceroute-based Internet Route change Analysis with Ensemble Learning},
author = {Raul Suzuki and Rodrigo Moreira and Pedro Henrique A. Damaso de Melo and Larissa F. Rodrigues Moreira and Flávio de Oliveira Silva},
journal= {arXiv preprint arXiv:2604.02361},
year = {2026}
}
Comments
Paper accepted for publication in Simp\'osio Brasileiro de Redes de Computadores e Sistemas Distribu\'idos (SBRC) 2026