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

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…
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Taxonomy
TopicsInternet Traffic Analysis and Secure E-voting · Wireless Signal Modulation Classification · Hate Speech and Cyberbullying Detection
Methodstravel james
