An Optimized K-means Clustering for Improving Accuracy in Traffic Classification

Abstract

With the explosive grown network traffic, the traditional port- and payload-based methods are insatiable for the requirements of privacy protection as well as the fast real-time classification for the today traffic classification. Here, a network traffic classification model based on both the Self-Organizing Maps (SOM) and the K-means fusion algorithm is proposed. In which, the traffic data is initially clustered by the SOM network to derive the cluster number and each cluster center value. Then those values are taken as the initial parameters to run the K-means algorithm, achieving optimal classification. As results compared with the traditional K-means algorithm, the initially clustering done by using the SOM network not only inherits its advantages of simple method and efficient processing, but also reduces time cost. Moreover, a significant improvement in coossification accuracy is achieved with our proposed algorithm.