Naive Bayes Based Network Traffic Classification Using Correlation Information

Traffic classification is of fundamental importance to numerous other network activities, from security monitoring to accounting, and from Quality of Service to providing operators with useful forecasts for long-term provisioning. Naive Bayes estimator is applied to categorize the traffic by application. Uniquely, this work capitalizes on hand-classified network data, using it as input to a supervised Naive Bayes estimator. A novel traffic classification scheme is used to improve classification performance when few training data are available. In the proposed scheme, traffic flows are described using the discretized statistical features and flow correlation information is modeled by bag-of-flow (BoF). A novel parametric approach for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. Then analyze the new classification approach and its performance benefit from both theoretical and empirical perspectives. Finally, a large number of experiments are carried out on large-scale real-world traffic datasets to evaluate the proposed scheme. The experimental results show that the proposed scheme can achieve much better classification performance than existing state-of-the-art traffic classification methods.