P2P traffic identification method based on an improvement incremental SVM learning algorithm

How to classify the data sets with vast information amount and large distribution fluctuation, which is always the research hotspot. This paper puts forward an improved SVM incremental learning algorithm by comparing the different incremental learning methods of SVM algorithm. In the algorithm, whether to violate the KTT conditions is regarded as an important basis for incremental data set. And the algorithm will be more efficient on the classification of SVM incremental sets through optimizing and improving itself. Then the paper compares the SVM-based re-training algorithm, the standard SVM incremental learning algorithm and the improved SVM incremental learning algorithm through identifying P2P network traffic. The experimental results show that the improved SVM incremental learning algorithm proposed in this article can save storage space and increase the accuracy of the identification of P2P traffic.