Internet Traffic Classification based on Min-Max Ensemble Feature Selection

Internet traffic classification is one of the key foundations for research works and traffic engineering in Internet. With the rapid increase of Internet applications and the number of Internet flow, the technique challenges are coupled with development of traffic classification all the time. Currently, the machine learning-based technique has attracted much attention, since it can address the issues that the usage of the dynamic port numbers and the encryption technique at the transport layer in traffic. As we have known, feature selection is one of the key problems in machine learning. In this paper, in order to improve the efficiency of feature selection in dealing with large scale traffic data problem, especially to imbalance classification problem that occur in traffic classification, a Min-Max Ensemble Feature Selection (M2-EFS) is proposed to deal with traffic data, which based on balanced data partition and min-max ensemble strategy. The experimental results demonstrate that the M2-EFS can obtain higher performance in most cases, and it could efficiently deal with imbalanced problems.