An Encryption Traffic Classification Method Based on ResNeXt

Abstract

Encryption traffic classification technology classifies traffic data according to different applications or different traffic types. It is one of the most important technologies to monitor network traffic security and collect network traffic information. In view of this, this paper proposes an encrypted traffic classification method based on the ResNeXt network. Ethernet headers and payloads in the traffic are removed in data preprocessing, and then the improved and simplified ResNeXt model is used to identify encrypted traffic data. The preprocessing method can greatly reduce the size of input data, save time, and achieve higher accuracy. The experimental results show that the classification accuracy of the proposed method for 12 types of encrypted traffic in "ICSX VPN-NonVPN" data set is 98.58%, and the average accuracy rate, recall rate and F1 score are 98.70%, 98.49%, and 0.9859, respectively.