# Early Detection of Network Service Degradation: An Intra-Flow Approach

arXiv: https://arxiv.org/abs/2407.06637 · HTML: https://mlsanity.com/paper/2407.06637/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 21 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Abstract (arXiv)

This research presents a novel method for predicting service degradation (SD) in computer networks by leveraging early flow features. Our approach focuses on the observable (O) segments of network flows, particularly analyzing Packet Inter-Arrival Time (PIAT) values and other derived metrics, to infer the behavior of non-observable (NO) segments. Through a comprehensive evaluation, we identify an optimal O/NO split threshold of 10 observed delay samples, balancing prediction accuracy and resource utilization. Evaluating models including Logistic Regression, XGBoost, and Multi-Layer Perceptron, we find XGBoost outperforms others, achieving an F1-score of 0.74, balanced accuracy of 0.84, and AUROC of 0.97. Our findings highlight the effectiveness of incorporating comprehensive early flow features and the potential of our method to offer a practical solution for monitoring network traffic in resource-constrained environments. This approach ensures enhanced user experience and network performance by preemptively addressing potential SD, providing the basis for a robust framework for maintaining high-quality network services.