Discern-XR高效识别元宇宙网络流量，助力服务优化。
Discern-XR: An Online Classifier for Metaverse Network Traffic
- 基于分段学习提取帧级统计特征，仅用4个应用层特征
- 在线训练算法使分类准确率提升7%，误报率降低
- 首个涵盖VR/AR/MR的元宇宙真实数据集，适合网络服务商参考
本文设计了一种专用于元宇宙网络流量分类的系统Discern-XR，帮助互联网服务提供商和路由器制造商提升元宇宙服务质量。通过分段学习，提出帧向量表示（FVR）与帧识别算法（FIA），从仅含4个应用层特征的原始网络数据中提取关键帧相关统计信息。进一步提出一种新型增广、聚合与保留在线训练（A2R-OT）算法，实现高效在线建模。此外，构建了包含虚拟现实（VR）游戏、VR视频、VR聊天、增强现实（AR）及混合现实（MR）流量的真实世界元宇宙数据集，提供全面基准。Discern-XR在性能上优于现有先进分类器7%，同时提升训练效率并降低误报率，成为当前元宇宙网络流量分类的最优方案。
原文摘要 · Abstract (English)
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.
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