TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Network Traffic Classification Based on Deep Learning |
| 영문제목(English Title) |
Network Traffic Classification Based on Deep Learning |
| 저자(Author) |
Junwei Li
Zhisong Pan
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| 원문수록처(Citation) |
VOL 14 NO. 11 PP. 4246 ~ 4267 (2020. 11) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
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
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| 키워드(Keyword) |
Traffic classification
deep learning
convolution neural network
stack auto encoder
long short-term memory network
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| 파일첨부 |
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