Internet traffic classification method based on selective clustering ensemble of mutual information

Abstract

Because it is difficult to label Internet traffic and the generalization ability of single clustering algorithm is weak,a selective clustering ensemble method based on Mutual Information(MI) was proposed to improve the accuracy of traffic classification.In the method,the Normalized Mutual Information(NMI) between clustering results of K-means algorithm with different initial cluster number and the distribution of protocol labels of training set was computed first,and then a serial of K which were the initial cluster number of K-means algorithm based on NMI were selected.Finally,the consensus function based on Quadratic Mutual Information(QMI) was used to build the consensus partition,and the labels of clusters were labeled based on a semi-supervised method.The overall accuracies of clustering ensemble method and single clustering algorithm were compared over four testing sets,and the experimental results show that the overall accuracy of clustering ensemble method can achieve 90%.In the proposed method,a clustering ensemble model was used to classify Internet traffic,and the overall accuracy of traffic classification along with the stability of classification over different dataset got enhanced.