TRACE: Traceroute-based Internet Route change Analysis with Ensemble Learning
Summary: arXiv:2604.02361v1 Announce Type: cross
Abstract: 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.
Introduction
The stability of Internet routing is essential for maintaining the quality of service in network communications. With the increasing complexity of Internet infrastructure, detecting routing instabilities has become more challenging. Traditional methods that rely on control plane information can be insufficient, leading to delays in identifying these critical changes. TRACE addresses this issue by utilizing traceroute latency data, providing a more independent and reliable approach to monitoring Internet routing.
Methodology
TRACE employs a robust feature engineering strategy that captures the temporal dynamics of Internet routing. The key components of the methodology include:
- Feature Engineering: The system utilizes rolling statistics and aggregated context patterns to effectively capture changes in latency data over time.
- Machine Learning Architecture: TRACE leverages a stacked ensemble of Gradient Boosted Decision Trees (GBDT), refined by a hyperparameter-optimized meta-learner. This architecture enhances the model’s ability to detect subtle changes in routing behavior.
- Class Imbalance Handling: One of the significant challenges in routing change detection is the inherent class imbalance, as rare routing events often go unnoticed. TRACE addresses this by calibrating decision thresholds specifically designed to improve detection of these rare events.
Results
The evaluation of TRACE demonstrates its effectiveness in detecting routing changes compared to traditional baseline models. The performance metrics indicate a significant improvement in the F1-score, showcasing the model’s ability to minimize false positives and false negatives. The results highlight the following:
- TRACE achieves an F1-score that surpasses existing methods, indicating greater accuracy in routing change detection.
- The system effectively identifies and responds to rare routing events that other models often miss.
- By using traceroute latency data, TRACE operates independently of control plane information, making it a versatile tool for network monitoring.
Conclusion
TRACE represents a significant advancement in the field of Internet routing analysis. By focusing on traceroute latency data and employing a sophisticated ensemble learning approach, it offers a reliable and independent solution for detecting routing instabilities. This work not only paves the way for more effective Internet monitoring but also highlights the potential of machine learning in addressing complex challenges in network management. Future research will aim to further refine the model and explore its application in real-world scenarios.