Research on Multi-layer Classification Method of Network Traffic Based on Machine Learning

Abstract

According to the network traffic characteristic group, this paper designs a kind of network traffic classification with coarse and fine two layers. The first layer based on the experience attribute selection and the fast multilayer clustering method, constructs the unsupervised classifier, which realizes the coarse classification of the network traffic dataset and forms the data subset; the second layer, based on the selected subset of data, constructs a strong supervised classifier, which realizes the fine classification of the network traffic data set, according to the ensemble learning method. Theoretical analysis and experiments show that this method can not only improve the accuracy of network traffic classification, but also reduce the time of classification greatly, which is important to improve the accuracy of small class classification of imbalanced network traffic data.