Year: 2024
Type: conference-paper
Abstract: 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 preliminary 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 early flow features and the potential of our method to offer a practical solution for monitoring network traffic in resource-constrained environments.
Authors: Balint Bicski, Adrian Pekar
Institution: Budapest University of Technology and Economics
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Innovation Fund
OpenAlex ID: W4405935509