A New Feature Selection Method for Internet Traffic Classification Using ML

Abstract

Abstract If 248 statistical features are used to characterize network traffic flows, the computation cost of classifier will be overlarge. The feature selection methods referenced here improve the accuracy of majority classes and meanwhile decrease the accuracy in minority classes as the cost. As a result, it brings about the multi-class imbalance problem. In this paper, main contributions include two aspects below. 1) An evaluation criterion based on information theory was proposed to assess how much do one feature bias towards one class. 2) A new feature selection method named BFS was proposed to reduce features and alleviate multi-class imbalance. BFS was compared with fast correlation-based filter (FCBF) and full feature set using Naive Bayes and ten skewed datasets. The results show that 1) BFS is more advantage to maintain the balance of multi-class classification results than FCBF, such as the reduction of g-mean is just about 8% using BFS, 2) classification accuracy of Naive Bayes using BFS can achieve to 90%.