Abstract: Accurate traffic classification is an effective guarantee for network management and security. Machine learning-based internet traffic classification became particularly notable in recent years, and feature selection had an important impact on the performance of machine learning. However, the feature selection subset that optimizes the overall classification performance is not the subset that optimizes the classification performance of a particular class, which reduces the upper limit of classification performance. Therefore,a new traffic classification model based on the improved one-versus-one method is proposed. In the new traffic classification model, traffic multi-classification task is split into multiple independent sub-tasks.Then feature selection and traffic classification are performed on any two classes of traffic,and the Stacking strategy is used to combine the results of all sub-tasks. The experiments show that the applications of several machine learning and feature selection algorithms to this model improve accuracies compared with those to the classical model.

Key words: one-versus-one, traffic classification, Stacking strategy, machine learning, feature selection