TRACE: Traceroute-based Internet Route change Analysis with Ensemble Learning - Borges, Raul Suzuki; Moreira, Rodrigo; Melo, Pedro Henrique A. Damaso de; Moreira, Larissa F. Rodrigues; Silva, Flávio de Oliveira (Anais do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC), 2026)
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.