Network Traffic Classification

Datasets (Google Drive)

• • 10 classes of mobile applications provided by the ACNS lab at the University of Dayton. 

  • 15 classes of data extracted from the ISCXVPN2016 dataset. (https://www.unb.ca/cic/datasets/vpn.html)

Related Publications

1. Jielun Zhang, Fuhao Li, and Feng Ye, “Sustaining the High Performance of AI-based Network Traffic Classification Models,” in IEEE/ACM Transactions on Networking, 2022.

2. Fuhao Li and Feng Ye, "Adaptive and Lightweight Network Traffic Classification for Edge Devices," in IEEE Transactions on Green Communications and Networking, doi: 10.1109/TGCN.2022.3165221. 

3. Jielun Zhang, Fuhao Li, Feng Ye, and Hongyu Wu, "Autonomous Unknown-Application Filtering and Labeling for DL-based Traffic Classifier Update," IEEE INFOCOM 2020.

4. Jielun Zhang, Fuhao Li, and Feng Ye, “An Ensemble-based Network Intrusion Detection Scheme with Bayesian Deep Learning,” IEEE ICC 2020. 

5. Pan Wang, Shuhang Li, Feng Ye, Zixuan Wang, Moxuan Zhang, “PacketCGAN: Exploring Class Imbalance for Encrypted Traffic Classification Using CGAN,” IEEE ICC 2020.

6. Jielun Zhang, Feng Ye, and Yi Qian “Intelligent and Application-aware Network Traffic Prediction in Smart Access Gateways”, IEEE Network, 2020.

7. Pan Wang, Xuejiao Chen, Feng Ye and Zhixin Sun, "A Survey of Techniques for Mobile Service Encrypted Traffic Classification Using Deep Learning," in IEEE Access, vol. 7, pp. 54024-54033, 2019.

8. Jielun Zhang, Fuhao Li, Hongyu Wu and Feng Ye, "Autonomous Model Update Scheme for Deep Learning based Network Traffic Classifiers," IEEE GLOBECOM 2019, Dec. 9-13, 2019, Waikoloa, HI.

9. Daidong Ying and Feng Ye, “Learning Based and Adaptive CE Approach for Power Allocation in Massive MIMO-NOMA," IEEE GLOBECOM 2019, Dec. 9-13, 2019, Waikoloa, HI.

10. Pan Wang, Feng Ye, Xuejiao Chen, Yi Qian, "DataNet: Deep Learning based Encrypted Network Traffic Classification in SDN Home Gateway," in IEEE Access, vol. 6, pp. 55380-55391, 2018 (https://github.com/runtrend08/Deep-Learning, kudos to Prof. Pan Wang)