关键词: 一维卷积神经网络, 流量分类, 数据预处理, 参数优化, 深度学习

Abstract: Aiming at the bottleneck problem of traditional machine learning algorithm for traffic classification, an application traffic classification algorithm based on one-dimensional convolution neural network model of deep learning algorithm is proposed. The data set of network traffic is preprocessed. In the data preprocessing stage, the irrelevant data fields are removed and the input characteristics of convolution neural network are satisfied. A new one-dimensional convolution neural network model is proposed, and the optimal classification model is constructed from the aspects of network structure, hyper-parametric space and parameter optimization. This model solves the problem of feature selection in traditional traffic classification algorithm based on machine learning by self-learning data features in convolution layer. Compared with the traditional one-dimensional convolution neural network model, the designed neural network model improves the classification accuracy by 16.4%, and the total classification time is saved by 71.48%. In addition, the class accuracy, recall rate and F1 score have been improved.

Key words: one-dimensional convolution neural network, network traffic classification, data preprocessing, parameter optimization, deep learning