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The classification of fifth-generation New-Radio (5G-NR) mobile networktraffic is an emerging topic in the field of telecommunications. It can beutilized for quality of service (QoS) management and dynamic resourceallocation. However, traditional approaches such as Deep Packet Inspection(DPI) can not be directly applied to encrypted data flows. Therefore, newreal-time encrypted traffic classification algorithms need to be investigatedto handle dynamic transmission. In this study, we examine the real-timeencrypted 5G Non-Standalone (NSA) application-level traffic classificationusing physical channel records. Due to the vastness of their features,decision-tree-based gradient boosting algorithms are a viable approach forclassification. We generate a noise-limited 5G NSA trace dataset with trafficfrom multiple applications. We develop a new pipeline to convert sequences ofphysical channel records into numerical vectors. A set of machine learningmodels are tested, and we propose our solution based on Light Gradient BoostingMachine (LGBM) due to its advantages in fast parallel training and lowcomputational burden in practical scenarios. Our experiments demonstrate thatour algorithm can achieve 95% accuracy on the classification task with astate-of-the-art response time as quick as 10ms.

Real-time Traffic Classification for 5G NSA Encrypted Data Flows With Physical Channel Records