Encrypted Network Traffic Classification in SDN using Self-supervised Learning

Abstract

Network traffic classification has a huge application in software-defined networking (SDN) where we talk about more control over the network traffic. With the increase of encrypted protocols in the network, the problem of traffic classification has become extremely challenging. Many researchers have proposed different techniques to do traffic classification. This demo paper presents an application of our proposed method for traffic classification in an SDN environment. The proposed method leverages one of the self-supervised learning approaches, an emerging field of deep learning, to classify network traffic. This paper shows that the proposed method can outperform the corresponding supervised approach by $\sim 2$% in terms of accuracy using data collected from an SDN testbed. Furthermore, an SDN application is developed to show that the trained model is able to classify real-time traffic.