Abstract

Network traffic classification is a core part of the network traffic management. Network management is a critical task since the various new applications are emerging every moment and increase in the number of users of an internet. Due to this problem, there is a need of internet traffic classification for smooth management of an internet by the internet service providers (ISP). Network traffic can be classified based on port, payload and statistical approach. In the proposed work, a novel method to represent internet traffic data based on clustering of feature vector using Multiple Kernel Fuzzy C-Means (MKFCM) is proposed. Further, feature vector of each cluster is used to build an interval valued representation (symbolic) using mean and standard deviation. In addition, this interval valued features are stored in knowledge base as a representative of the cluster. Further, to classify the symbolic interval data, we used symbolic classifier. To validate the effectiveness of the proposed model, experimentation is conducted on standard Cambridge University internet traffic dataset. Further, the proposed symbolic classifier compared with other existing classifiers such as Naïve Bayes, KNN and SVM classifier. The experiment outcome infers that; the proposed symbolic representation classifier performs better than other classifiers.