摘要
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.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Key words
encrypted traffic/traffic classification/edge weight graph neural network/pointwise mutual information(PMI)/threshold filtering mechanism/deep learning分类
信息技术与安全科学
