Real-time Traffic Classification for 5G NSA Encrypted Data Flows With
  Physical Channel Records
Xiao Fei Philippe Martins Jialiang Lu
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.