This repository demonstrates the encrypted network traffic classification in SDN environment. A testbed is created using Mininet in this project. A RYU controller application is developed to classify network traffic in real-time. The controller application uses a machine learning (ML) model to classify the real-time traffic. The ML model is trained according to our proposed method mentioned in [4].
The following diagram shows the system overview:
The following video shows a demo of the application.
[1] Mininet: http://mininet.org/
[2] D-ITG: https://allstar.jhuapl.edu/repo/p1/amd64/d-itg/doc/d-itg-manual.pdf
[3] RYU controller: https://ryu.readthedocs.io/en/latest/
[4] M. S. Towhid and N. Shahriar, "Encrypted Network Traffic Classification using Self-supervised Learning," 2022 IEEE 8th International Conference on Network Softwarization (NetSoft), 2022, pp. 366-374, doi: 10.1109/NetSoft54395.2022.9844044.