Traffic classification combining flow correlation and ensemble classifier

Traffic classification has wide applications in network measurements, network security and quality of service. Recent research tends to apply machine learning methods based on flow statistical features to improve traffic classification performance. In this paper, we propose a novel non-parametric approach for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. Meanwhile, our system employs a lightweight modular architecture, which combines a series of simple linear binary classifiers, each of which can be efficiently implemented and trained on vast amounts of flow data in parallel, to achieve scalability while attaining high accuracy. A large number of experiments are carried out on real traffic data to validate the proposed approach. The results show that the traffic classification performance can be improved significantly while meeting the scalability and stability requirements of large networks.