Classification method of IPv6 traffic based on convolutional neural network

Abstract This paper proposes a method of IPv6 traffic classification based on convolutional neural network. It aims to solve the problems of low accuracy, high dependence on traffic feature selection, inefficiency in IPv6 traffic classification technology based on machine learning, and the lack of r...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1883; no. 1; p. 12088
Main Authors Ying, Pan, Shihui, Wang, Xing, Xu
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.04.2021
Subjects
Artificial neural networks
Classification
Convolution
IP (Internet Protocol)
Machine learning
Neural networks
Physics
Traffic models
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Summary:Abstract This paper proposes a method of IPv6 traffic classification based on convolutional neural network. It aims to solve the problems of low accuracy, high dependence on traffic feature selection, inefficiency in IPv6 traffic classification technology based on machine learning, and the lack of research on IPv6 traffic classification methods. The method transforms IPv6 traffic into two-dimensional matrix as input of convolution neural network. Then, it constructs a traffic classification model of feature autonomous learning. The model consist of one input layer, two convolutional pooling layers, one full connection layer and one output layer. The experimental results show that the designed model has a good performance in IPv6 traffic classification.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1883/1/012088