A continual encrypted traffic classification algorithm based on WGAN

Abstract

With the constant updating of applications and the emergence of various encryption technologies, a large amount of new encrypted network traffic is generated every day. Therefore, it is a challenging task to achieve continual learning of encrypted traffic. Existing encrypted traffic classification techniques can only handle a fixed number of traffic classes, which is not applicable to real network environments. In this paper, we proposed a continual encrypted traffic classification method based on WGAN, called CETC. The method takes advantage of the powerful data generation capabilities of WGAN to model the data distribution of encrypted traffic. When learning from a new traffic class, the samples from the old class is generated by WGAN to train the new classifier. We use the ISCX VPN-nonVPN dataset to test the performance of CETC. Experimental results show that WGAN can generate high-quality samples of encrypted traffic and the accuracy of CETC is higher than 93%. With its efficient and continual learning capability, CETC can be applied to various encrypted traffic detection and management systems.