Segmented-Learning for Metaverse Network Traffic Classification

We propose a novel two-staged segmented learning framework to enhance network traffic classification (NTC) for 5G and beyond (B5G)-driven enhanced mobile broadband (eMBB) applications, including Metaverse traffic. The first stage improves classification speed and accuracy for eMBB traffic, and the second stage extends its capability to classify the more complex and dynamic Metaverse network traffic. We introduce Essential Vector Representation (EVR) and Frame Vector Representation (FVR) feature engineering methods. These methods reduce inference time and preserve privacy by leveraging application-level features such as transmission time, packet length, direction, and inter-arrival time. The outputs from EVR and FVR are classified using our Augmentation, Aggregation, and Retention-Online Training (A2R-OT) algorithm, which enhances adaptive online learning, improving accuracy and efficiency. Additionally, we construct a comprehensive real-world Metaverse network traffic dataset to address the lack of publicly available Metaverse traffic data. To our knowledge, this is the first framework to integrate eMBB and Metaverse traffic classification. Our approach achieves a 6% improvement over state-of-the-art solutions, advancing network traffic management (NTM) for B5G networks.