Towards selecting optimal features for flow statistical based network traffic classification

The network traffic classification is one of the most fundamental work in the network measurement and management, and this problem is more and more impact as the network scale grows. Many methods are proposed by researchers, but methods based on flow statistics seem more popular than the others. In this paper, we proposed a novel method based on refined flow statistical features. The new statistics, skewness and kurtosis, and new flow statistical features, payload length, were introduced into raw feature set firstly. Then, with the consideration of efficiency in the classification stage, the feature selection was used on the raw feature set to get an optimal feature set and the feature selection are mainly based on the K-means clustering algorithm. The comparison experiment results show that the proposed optimal feature set reaches the same precision level with half time consuming and internal cluster distance when compared with the raw set.