Design of Web Application Traffic Classification Model Based on Convolution Neural Network 


Vol. 44,  No. 6, pp. 1113-1120, Jun.  2019
10.7840/kics.2019.44.6.1113


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  Abstract

The basic role of network management is to provide quality of service suitable for users. Accurate application traffic classification is essential to provide adequate quality of service and to ensure a secure network environment. The existing traffic classification methods are port-based classification methods, payload-based classification methods and statistic information-based classification methods. However, due to the emergence of applications that generate packets with dynamic ports or encrypted payloads, the limitations of existing traffic classification techniques are occurred. In this paper, in order to address these limitations, we propose an application traffic classification model applying the convolution neural network algorithm which is one of the machine learning algorithms for 10 kinds of web application traffic(Baidu, Bing, Daum, Google, Kakaotalk, Nate, Naver, Yahoo, Youtube, Zum). The proposed model achieves 100% train classification accuracy and 99.4% validation classification accuracy.

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  Cite this article

[IEEE Style]

S. Ji, U. Baek, M. Shin, B. Chae, H. Moon, M. Kim, "Design of Web Application Traffic Classification Model Based on Convolution Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 6, pp. 1113-1120, 2019. DOI: 10.7840/kics.2019.44.6.1113.

[ACM Style]

Se-Hyun Ji, Ui-Jun Baek, Mu-Gon Shin, Byeong-Min Chae, Ho-Won Moon, and Myung-Sup Kim. 2019. Design of Web Application Traffic Classification Model Based on Convolution Neural Network. The Journal of Korean Institute of Communications and Information Sciences, 44, 6, (2019), 1113-1120. DOI: 10.7840/kics.2019.44.6.1113.

[KICS Style]

Se-Hyun Ji, Ui-Jun Baek, Mu-Gon Shin, Byeong-Min Chae, Ho-Won Moon, Myung-Sup Kim, "Design of Web Application Traffic Classification Model Based on Convolution Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 6, pp. 1113-1120, 6. 2019. (https://doi.org/10.7840/kics.2019.44.6.1113)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880