Tree-RNN: Tree structural recurrent neural network for network traffic classification

Abstract

Network traffic classification plays an important role in network monitoring and network management. With the continuous development of network technology, traditional methods of traffic classification have more limitations in accuracy to deal with encrypted traffic. Fortunately, deep neural network (DNN) is an effective method for handling traffic classification due to its ability to learn inherent data features. However, this method generally classifies network traffic with only the single classifier, which makes it relatively less effective in some classes for the problem of large classification. In this paper, we propose a tree structural recurrent neural network (Tree-RNN), which divides a large classification into small classifications by using the tree structure. A specific classifier is set for each small classification after division. With multiple classifiers employed, Tree-RNN can complement each other in classification performance, and the problem of the single classifier is solved. Since multiple classifiers are all end-to-end frameworks, Tree-RNN can automatically learn the nonlinear relationship between input data and output data without feature extraction. To verify the validity of our model, we compare Tree-RNN with state-of-the-art methods using the ISCX public traffic dataset. Experimental results show that Tree-RNN can achieve higher performance in less training time. The average accuracy of Tree-RNN is 4.88% higher than other state-of-the-art methods, and it has higher average precision and average recall.