Machine Learning-Based Mobile Traffic Classification for QoS-Oriented Network Management

Abstract

The increasing complexity and volume of mobile network traffic present significant challenges to maintain consistent Quality of Service (QoS) across diverse applications. Accurate traffic classification enables application-aware resource allocation by distinguishing applications with different bandwidth, latency, and reliability requirements. Traditional classification techniques, including port-based identification and Deep Packet Inspection (DPI) have become inadequate and less effective due to widespread encryption, port masquerading, and growing privacy concerns. This paper presents a supervised learning-based approach for application-level network traffic classification specifically as a foundation for QoS optimization in future 5G networks. Since publicly available labeled 5G traffic datasets remain limited, this study uses the MIRAGE-2019 mobile traffic dataset as a proxy dataset to evaluate the proposed classification framework. A Random Forest classifier was implemented using flow-level statistical features extracted from the mobile application traffic. The framework further incorporates a rule-based QoS policy mapping informed by RFC 4594 DiffServ service class guidelines to assign application-specific priority levels, bandwidth requirements, latency sensitivity, and jitter tolerance. Experimental evaluation achieved an overall classification Accuracy of 71.83%, a Macro F1-score of 0.6701, and a Weighted F1-score of 0.7227 across twenty mobile applications. Although the experiments were conducted using a pre-5G mobile traffic dataset, the results demonstrate that supervised machine learning can effectively classify encrypted mobile application traffic and provide a practical foundation for application-aware QoS policy enforcement in future 5G and next-generation mobile networks.