Fine-Grained Encrypted Traffic Classification Using Dual Embedding and Graph Neural Networks
Zhengyang Liu, Qiang Wei, Qisong Song, Changxu Duan
Abstract
Encrypted traffic classification poses significant challenges in network security due to the growing use of encryption protocols, which obscure packet payloads. This paper introduces a novel framework that leverages dual embedding mechanisms and Graph Neural Networks (GNNs) to model both temporal and spatial dependencies in traffic flows. By utilizing metadata features such as packet size, inter-arrival times, and protocol attributes, the framework achieves robust classification without relying on payload content. The proposed framework demonstrates an average classification accuracy of 96.7%, F1-score of 96.0%, and AUC-ROC of 97.9% across benchmark datasets, including ISCX VPN-nonVPN, QUIC, and USTC-TFC2016. These results mark an improvement of up to 8% in F1-score and 10% in AUC-ROC compared to state-of-the-art baselines. Extensive experiments validate the framework’s scalability and robustness, confirming its potential for real-world applications like intrusion detection and network monitoring. The integration of dual embedding mechanisms and GNNs allows for accurate fine-grained classification of encrypted traffic flows, addressing critical challenges in modern network security.