Advancing LLM-based 5G traffic classification: a multi-layer evaluation of performance and reasoning reliability

Abstract The rapid proliferation of 5G networks has enabled a wide range of data-intensive applications, including video streaming, online gaming, metaverse platforms, and real-time communication services. Understanding the behavior and characteristics of such heterogeneous traffic is essential for efficient network management, resource allocation, and quality of service (QoS) optimization. However, most existing studies rely on simulated or synthetic datasets, limiting their applicability to real-world scenarios. This paper presents a comprehensive data-driven analysis of 5G traffic using a large-scale real-world dataset collected from commercial mobile networks. The dataset consists of over 328 h of traffic captured from diverse applications, including Netflix, YouTube Live, Zoom, Roblox, and cloud gaming platforms, using mobile-based packet capture tools without specialized hardware. The collected data is processed into a time-series format with packet-level features, enabling detailed traffic characterization and analysis. We investigate the effectiveness of machine learning and large language models (LLMs) in classifying and analyzing 5G traffic patterns across multiple application categories. Experimental results reveal that traditional structured machine learning approaches outperform LLM-based models in handling network traffic classification tasks, particularly in multi-class and imbalanced scenarios. Furthermore, LLM-based methods exhibit high rates of inconsistency and prediction bias, highlighting their limitations when applied to structured network data. The findings of this study provide practical insights into the suitability of different AI approaches for real-world 5G traffic analysis. The proposed framework contributes to the development of more reliable and efficient traffic classification systems, supporting advanced network optimization, intelligent traffic management, and future 5G/6G deployments.