摘要：At present traffic classification is widely concerned in many research fields such as network security, traffic scheduling and traffic accounting. How to identify network traffic fast and accurately is a very meaningful thing. But most machine learning based methods have a lower speed and efficiency, and can not guarantee their stability and usability. For this reason a Principal Component Analysis (PCA) based method is proposed in the paper. At first the method use Fast Correlation-Based Filter (FCBF) algorithm to filter training data set to obtain suitable flow attributes. Then these flow attributes are processed by PCA to build feature subspace for each flow class. After that a nearest neighbor rule is used to accurately identify flow class of testing traffic sample. In the end some experiments on public data sets are done to compare performance with some existing methods. The experimental results show that the PCA based method has higher accuracy, stability and faster speed than Naive Bayes (NB) estimation method and Naive Bayes Kernel (NBK) estimation method.
