Accurate network traffic classification is vital for managing modern applications with strict Quality of Service (QoS) demands, such as edge computing, real-time XR, and autonomous systems. While recent advances in application-level classification show high accuracy, they often miss fine-grained in-app QoS variations critical for service differentiation. This paper proposes a hierarchical graph neural network (GNN) framework that combines a three-level graph representation with an automated QoS-aware assignment algorithm. The model captures multi-scale temporal patterns via packet aggregation, time-window clustering, and session-level behavior modeling. QoS priorities are derived using five key metrics (bandwidth, jitter, packet stability, burst frequency, and burst stability), processed through logarithmic transformation and weighted ranking. Evaluations across 14 usage scenarios from YouTube, Prime Video, TikTok, and Zoom show that the proposed GNN significantly outperforms state-of-the-art methods in service-level classification. The QoS-aware assignment further refines classification to enhance user experience. This work advances QoS-aware traffic classification by enabling precise in-app usage differentiation and adaptive service prioritization in dynamic network environments.

翻译：精确的网络流量分类对于管理具有严格服务质量（QoS）需求的现代应用（如边缘计算、实时扩展现实和自主系统）至关重要。尽管应用级分类的最新进展显示出较高的准确性，但它们往往忽略了对于服务差异化至关重要的、细粒度的应用内QoS变化。本文提出了一种分层图神经网络（GNN）框架，该框架结合了三级图表示与自动化的QoS感知分配算法。该模型通过数据包聚合、时间窗口聚类和会话级行为建模来捕获多尺度时间模式。QoS优先级使用五个关键指标（带宽、抖动、数据包稳定性、突发频率和突发稳定性）推导得出，并通过对数变换和加权排序进行处理。在来自YouTube、Prime Video、TikTok和Zoom的14个使用场景中的评估表明，所提出的GNN在服务级分类方面显著优于现有最先进的方法。QoS感知分配进一步细化了分类以提升用户体验。这项工作通过实现动态网络环境中精确的应用内使用差异化和自适应服务优先级排序，推进了QoS感知的流量分类。