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This repository describes the demonstration of 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.

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encrypted_network_traffic_classification_in_sdn_cbr's Introduction

Encrypted Network Traffic Classification in SDN

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:

Demo

The following video shows a demo of the application.

References

[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.

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