Summary
Asia-Pacific Network Operations and Management Symposium
2022
Session Number:TS1
Session:
Number:TS1-04
MISCNN: A Novel Learning Scheme for CNN-Based Network Traffic Classification
Ui-Jun Baek, Boseon Kim, Jee Tae Park, Jeong-Woo Choi, Myung-Sup Kim,
pp.-
Publication Date:2022/09/28
Online ISSN:2188-5079
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Summary:
By the rapid development of the Internet and online applications, traffic classification has changed to an important topic in the field of network management. Although many studies have been conducted in recent years, designing a robust classification model remains a major challenge. Even though previous researches have focused on changing the layer structure within the deep learning model, they do not consider the input shape that best represents the traffic. To this end, a new traffic classification method is presented in this paper that aims to utilize various input shape that can be derived from fixed-length packet bytes. The proposed method utilized MISCNN (Multi Input Shape Convolution Neural Network) to generate robust traffic classification model that can be used in many domains. Various experiments were carried out to verify superiority of proposed method for the tasks of traffic classification and application identification. According to the obtained results, MISCNN achieved higher score compared to previous researches that utilized only 2D square input shape and 1D linear input shape on the ISCX VPN-nonVPN dataset.