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

TL;DR

This paper presents a real-time classification method for encrypted 5G NSA traffic using physical channel records and gradient boosting, achieving high accuracy and low latency for network management.

Contribution

It introduces a novel pipeline converting physical channel data into features and applies LGBM for fast, accurate encrypted traffic classification in 5G networks.

Findings

Achieves 95% classification accuracy

Response time as low as 10 milliseconds

Effective for real-time network management

Abstract

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