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

arXiv: https://arxiv.org/abs/2604.02361 · HTML: https://mlsanity.com/paper/2604.02361/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 21 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [AI-Native Multi-Access Future Networks -- The REASON Architecture](https://mlsanity.com/paper/2411.06870/) (2024)
- [BigBen: Telemetry Processing for Internet-wide Event Monitoring](https://mlsanity.com/paper/2011.10911/) (2020)
- [BGP-Multipath Routing in the Internet](https://mlsanity.com/paper/2107.10938/) (2021)
- [Understanding Routing-Induced Censorship Changes Globally](https://mlsanity.com/paper/2406.19304/) (2024)

## Abstract (arXiv)

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.