文献类型：期刊文献

英文题名：Network Traffic Classification Based on Deep Learning

作者：Li, Junwei[1,2];Pan, Zhisong[1]

第一作者：Li, Junwei

通讯作者：Pan, ZS[1]

机构：[1]Army Engn Univ, Inst Command Control Engn, Nanjing 210007, Peoples R China;[2]XinXiang Univ, Inst Comp & Informat Engn, Xinxiang 453003, Henan, Peoples R China

第一机构：Army Engn Univ, Inst Command Control Engn, Nanjing 210007, Peoples R China

通讯机构：[1]corresponding author), Army Engn Univ, Inst Command Control Engn, Nanjing 210007, Peoples R China.

年份：2020

卷号：14

期号：11

起止页码：4246-4267

外文期刊名：KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS

收录：;EI(收录号：20204909577804);Scopus(收录号：2-s2.0-85097030928);WOS:【SCI-EXPANDED(收录号:WOS:000595864500001)】；

基金：This research was partially supported by the National Key Research and Development Plan Key Projects of China under Grant No. 2017YFB0802800 and the National Natural Science Foundation of China Grant No. 61473149.

语种：英文

外文关键词：Traffic classification; deep learning; convolution neural network; stack auto encoder; long short-term memory network

摘要：As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.