关键词: 网络流量分类, 支持向量机, 半监督, 增量学习, 协同训练

Abstract: In order to solve low accuracy, large time consumption and limited application range in traditional network traffic classification, a semisupervised network traffic classification method of Support Vector Machine (SVM) was proposed. During the training of SVM, it determined the support vectors from the initial and new sample set by using incremental learning technology, avoided unnecessary repetition training, and improved the situation of original classifiers’ low accuracy and timeconsuming as a result of new samples that appeared. This paper also proposed an improved Tri-training method to train multiple classifiers, and a large number of unlabeled samples and a small amount of labeled samples were used to modify the classifiers, which reduced auxiliary classifier’s noise data and overcame the strict limitation of sample types and traditional Coverification for classification methods. The experimental results show that the proposed algorithm has excellent accuracy and speed in traffic classification.

Key words: network traffic classification, Support Vector Machine (SVM), semisupervised, incremental learning, Tri-training