TRACE: Traceroute-based Internet Route change Analysis with Ensemble Learning
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866911564248907776 |
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| author | Suzuki, Raul Moreira, Rodrigo de Melo, Pedro Henrique A. Damaso Moreira, Larissa F. Rodrigues Silva, Flávio de Oliveira |
| author_facet | Suzuki, Raul Moreira, Rodrigo de Melo, Pedro Henrique A. Damaso Moreira, Larissa F. Rodrigues Silva, Flávio de Oliveira |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02361 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TRACE: Traceroute-based Internet Route change Analysis with Ensemble Learning Suzuki, Raul Moreira, Rodrigo de Melo, Pedro Henrique A. Damaso Moreira, Larissa F. Rodrigues Silva, Flávio de Oliveira Networking and Internet Architecture Artificial Intelligence Machine Learning 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. |
| title | TRACE: Traceroute-based Internet Route change Analysis with Ensemble Learning |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.02361 |