Qisong Song
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Qisong Song has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Chaoyuan Duan
School of Cyberspace Security, Zhengzhou University, Zhengzhou 450000, China
1 shared publication - Qiang Wei
Information Engineering University, Zhengzhou, China
1 shared publication - Zhengyang Liu
School of Cyberspace Security, Zhengzhou University, Zhengzhou 450000, China
1 shared publication
Latest Publications
- Open Access
- 17 February 2025
- Published by MDPI AG in Electronics
- Vol. 14 (4) , 778
Fine-Grained Encrypted Traffic Classification Using Dual Embedding and Graph Neural Networks
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.
- 17 February 2025
- Published by MDPI AG in Electronics
- Vol. 14 (4) , 778
Open AccessFine-Grained Encrypted Traffic Classification Using Dual Embedding and Graph Neural Networks
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.