Abstract: This paper proposes an edge weightaware graph neural network (EWGNN) model for encrypted traffic classification. By introducing an innovative edgeweighting mechanism, the model effectively leverages graph structural information to distinguish the importance of different edges for classification tasks, thereby enhancing feature extraction capabilities while reducing noise interference. The EWGNN architecture comprises four core components: a dualbranch embedding structure, a GNNbased traffic representation encoder, a crossgating feature interaction mechanism, and an endtoend classification module. Experimental results demonstrate that EWGNN achieves 94.75% accuracy, 95.12% precision, 94.83% recall, 94.97% F1score, and 0.954 AUC on the ISCXVPN dataset, significantly outperforming baseline models. Ablation studies further validate the effectiveness of the edgeweighting mechanism, showing over a 1.5% performance improvement across all metrics when activated. Future work will focus on extending application scenarios, optimizing the model architecture and training strategies, and integrating cuttingedge techniques to address challenges in encrypted traffic classification.

Key words: encrypted traffic, traffic classification, edge weight graph neural network, pointwise mutual information (PMI), threshold filtering mechanism

摘要： 提出一种面向边权重的图神经网络(edge weightaware graph neural network, EWGNN)模型，用于加密流量分类.该模型通过创新的边权重机制，精细化利用图结构信息，有效区分不同边对分类任务的重要性，从而增强对图结构特征的捕捉能力并减少噪声干扰.EWGNN模型主要由4个核心组件构成：双分支嵌入结构、图神经网络流量表征编码器、交叉门控特征交互机制和端到端分类模块.实验结果表明，EWGNN模型在ISCXVPN数据集上的准确率、精确率、召回率和F1分数分别达到94.75%,95.12%,94.83%,94.97%，AUC值为0.954，显著优于其他比较模型.消融实验进一步证实了边权重机制的有效性，启用该机制后模型的各项性能指标均得到超过1.5%的提升.未来工作将集中在拓展模型的应用场景、优化模型结构和训练方法，结合前沿技术提升模型性能，以应对加密流量分类领域的挑战.

关键词: 加密流量, 流量分类, 边权重图神经网络, 点互信息, 阈值过滤机制, 深度学习