Multi-stage feature selection for on-line flow peer-to-peer traffic identification

Classification of bandwidth-heavy Internet traffic is important for network administrators to throttle network of heavy-bandwidth applications traffic. Statistical methods have been previously proposed as promising method to identify Internet traffic based on packet statistical features. The selection of statistical features still plays an important role for accurate and timely classification. In this work, we propose an approach based on feature selection methods and analytic methods (scatter, one-way analysis of variance) in order to provide optimal features for on-line P2P traffic detection. Feature selection algorithms and machine learning algorithms were implemented using WEKA tool for available traces from University of Brescia, University of Aalborg and University of Cambridge. Experimental results show that the proposed method is able to achieve up to 99.5% accuracy with just six on-line statistical features. These results perform better than other existing approaches in term of accuracy and the number of features.