Discern-XR: An Online Classifier for Metaverse Network Traffic

TL;DR

Discern-XR is an innovative online classifier designed for Metaverse network traffic, utilizing segmented learning and novel algorithms to improve accuracy and efficiency in real-time classification of diverse Metaverse data types.

Contribution

The paper introduces Discern-XR, a novel online classification framework with new algorithms and a comprehensive dataset, advancing real-time Metaverse traffic identification.

Findings

Outperforms existing classifiers by 7% in accuracy.

Reduces false-negative rates significantly.

Enhances training efficiency for real-time applications.

Abstract

In this paper, we design an exclusive Metaverse network traffic classifier, named Discern-XR, to help Internet service providers (ISP) and router manufacturers enhance the quality of Metaverse services. Leveraging segmented learning, the Frame Vector Representation (FVR) algorithm and Frame Identification Algorithm (FIA) are proposed to extract critical frame-related statistics from raw network data having only four application-level features. A novel Augmentation, Aggregation, and Retention Online Training (A2R-OT) algorithm is proposed to find an accurate classification model through online training methodology. In addition, we contribute to the real-world Metaverse dataset comprising virtual reality (VR) games, VR video, VR chat, augmented reality (AR), and mixed reality (MR) traffic, providing a comprehensive benchmark. Discern-XR outperforms state-of-the-art classifiers by 7% while improving training efficiency and reducing false-negative rates. Our work advances Metaverse network traffic classification by standing as the state-of-the-art solution.