Classification method of IPv6 traffic based on convolutional neural network
Pan Ying, Wang Shihui, Xu Xing · Journal of Physics Conference Series · 2021
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.