VPN encrypted traffic identification model based on improved residual network

Abstract

Accurate classification of network traffic is the research basis for tasks such as network management, service quality optimization, abnormal traffic detection, etc. Aiming at the problems of incomplete feature extraction of existing methods, a VPN encrypted traffic identification model based on improved residual network is proposed. The model takes the conversation composed of bidirectional flows as the analysis object, first uses the multi-layer convolutional network to extract the spatial structure features of the conversation, then introduces the improved residual unit to increase the width of the backbone network, and uses 1x1 and 3x3 different sizes of convolution kernels on the same level to obtain different scale feature information, and improve the model’s feature representation ability. Finally, the extracted features are expanded into one-dimensional vectors and Softmax funciton is used to classify the encrypted traffic. The Experimental results on public VPN-non VPN dataset show that the proposed model performs better than traditional identification methods, and compared with the well-known methods, the accuracy, precision and recall metrics are improved by 3.32%, 2.65% and 3.79%, respectively.