Detecting Route Changes from RTT Measurements using XGBoost and Feature Engineering - Andrade, Thiago Prado de Azevedo; Trotti, Bruno Llacer; Menasché, Daniel Sadoc (Anais do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC); 2026: Anais do XLIV Simpósio Brasileiro de Redes de Computadores e Sistemas DistribuídosDO - 10.5753/sbrc.2026.19266, 2026)
We address the route-change detection task of the Second Data Challenge CT-Mon/RNP 2025. Given consecutive traceroute measurements for a fixed origin–destination pair, the goal is to predict whether a route change occurred, using only last-hop RTT samples and probing metadata under extreme class imbalance. We propose a lightweight, pair-centric feature engineering pipeline that summarizes short-horizon RTT dynamics (e.g., step changes and deviations from rolling baselines), sampling irregularity, and reply/probe ratios, and trains a global XGBoost classifier with imbalance-aware weighting and validation-based threshold selection. On a held-out validation split, the resulting model achieves high discriminative performance (AUC-ROC 0.9956) and operates at a recall-oriented point (recall 0.93, precision 0.60 for route changes), making it suitable for monitoring and alerting applications. We further analyze gain-based feature importance, showing that consecutive RTT step differences and short-window deviations dominate the predictive signal.