计算技术与自动化Issue(1)：116-120,5.
改进的图半监督支持向量机用于 P2P 网络流识别
Improved Graphic Semi-supervised SVM for P2P Network Traffic Identification
毕孝儒 1侯爱莲2
作者信息
- 1. 四川外国语大学 重庆南方翻译学院 管理学院，重庆 401120
- 2. 中国人民银行 长沙中心支行，湖南 长沙 410005
- 折叠
摘要
Abstract
In P2P network traffic identification,aiming at the problems that passive machine learning needs a lot of la-beled training data ,an improved graphic semi-supervised learning method was proposed.and with SVM used in P2P net-work traffic identification.Gauss kernel function of self-regulation was applied for calculating similar distance of graphic model.Meanwhile,in the course of label propagation,local distribution information of training samples was added to get label of unlabeled samples.Finally,the labeled samples were used to train SVM for P2P network traffic identification.Simulation shows that the method can give consideration to all the information of training samples,effectively improve accuracy rate of P2P network traffic identification and greatly reduce the cost of labeling training samples.关键词
P2P网络流识别/图/半监督学习/标记传播Key words
P2P network traffic identification/graph/semi-supervised learning/label propagation分类
信息技术与安全科学引用本文复制引用
毕孝儒,侯爱莲..改进的图半监督支持向量机用于 P2P 网络流识别[J].计算技术与自动化,2015,(1):116-120,5.
