Encrypted Traffic Classification with a Convolutional Long Short-Term Memory Neural Network

Abstract

With the rapidly emerging encryption techniques for network traffic, the classification of encrypted traffic has increasingly become significantly important in network management and security. In this paper, we propose a novel deep neural network that combines both the convolutional network and the recurrent network to improve the accuracy of the classification results. The convolutional network is used to extract the packet features for a single packet. The recurrent network is trained to pick out the flow features based on the inputs of the packet features of any three consecutive packets in a flow. The proposed model surpasses the existing studies which ask for the first packets of a flow, and it provides more flexibility in real practice. We compare our model with the existing work under deep learning for encrypted traffic classification, based on the public dataset. The experimental results show that our model outperforms the state-of-the-art work in terms of both higher efficiency and effectiveness.