ntc-gnn

The repository document the source code of the following works:

• Huoh, T. L., Luo, Y., Li, P., & Zhang, T. (2022). Flow-based encrypted network traffic classification with graph neural networks. IEEE Transactions on Network and Service Management.
• Huoh, T. L., Luo, Y., & Zhang, T. (2021, May). Encrypted network traffic classification using a geometric learning model. In 2021 IFIP/IEEE International Symposium on Integrated Network Management (IM) (pp. 376-383). IEEE.

Getting started

• GNN_model.ipynb

This file provides the GNN training pipeline from loading the data into a graph to train and validate the GNN.

• preprocessing_function.ipynb

This file provides the function for preprocessing a .pcap file.

Prerequisites

• Graph Nets library
• tensorflow 2.2.0
• numpy 1.18.1
• scipy 1.4.1
• cuda 10.1

Dataset

VPN-nonVPN dataset (ISCXVPN2016): https://www.unb.ca/cic/datasets/vpn.html

Cite

Access our journal paper here:

• Flow-based encrypted network traffic classification with graph neural networks

@article{huoh2022flow,
  title={Flow-based encrypted network traffic classification with graph neural networks},
  author={Huoh, Ting-Li and Luo, Yan and Li, Peilong and Zhang, Tong},
  journal={IEEE Transactions on Network and Service Management},
  year={2022},
  publisher={IEEE}
}

---

Access our conference paper here:

• Encrypted network traffic classification using a geometric learning model

@inproceedings{huoh2021encrypted,
  title={Encrypted network traffic classification using a geometric learning model},
  author={Huoh, Ting-Li and Luo, Yan and Zhang, Tong},
  booktitle={2021 IFIP/IEEE International Symposium on Integrated Network Management (IM)},
  pages={376--383},
  year={2021},
  organization={IEEE}
}