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.

翻译：第五代新无线电（5G-NR）移动网络流量的分类是电信领域的新兴课题。该技术可用于服务质量（QoS）管理与动态资源分配。然而，深度包检测（DPI）等传统方法无法直接应用于加密数据流。因此，需要研究新型实时加密流量分类算法以应对动态传输。本研究中，我们探讨了基于物理信道记录的实时加密5G非独立组网（NSA）应用层流量分类方法。鉴于其特征维度的广泛性，基于决策树的梯度提升算法成为可行的分类方案。我们生成了一个包含多种应用流量的噪声受限5G NSA轨迹数据集，并开发了将物理信道记录序列转换为数值向量的新流程。通过测试多种机器学习模型，我们最终提出基于轻量梯度提升机（LGBM）的解决方案，该方案具有快速并行训练和实际场景中计算负担低的优势。实验表明，该算法在分类任务上可达到95%的准确率，同时实现低至10ms的业界最优响应时间。