Network Traffic Classification Using Correlation Information
Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine learning techniques to flow statistical feature based classification methods. The Nearest-Neighbor (NN) based method has exhibited superior classification performance. It also has several important advantages, such as no requirements of training procedure, no risk of over-fitting of parameters, and naturally being able to handle a huge number of classes. However, the performance of NN classifier can be severely affected if the size of training data is small. In this paper, the authors propose a novel non-parametric approach for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process.