计算机工程2012，Vol.38Issue(17)：171-173,3.DOI:10.3969/j.issn.1000-3428.2012.17.047
一种基于SVM后验概率的网络流量识别方法
A Network Flow Identification Method Based on SVM Posteriori Probability
摘要
Abstract
In order to solve the crux for sample's label and implement active learning in network environment, the network flow identification method is presented by using Support Vector Machine(SVM) with posteriori probability. The sample's posteriori probability is got by the output of SVM and Sigmoid function. It uses the larger of the two 2 class probability information entropy to measure the sample Effect Score(ES). By means of SVM and uncertainty sampling strategy, it realizes the active learning process, and traffic identification's model is formed. Experimental results show that the method can achieve better identification result.关键词
流量识别/主动学习/支持向量机/熵/不确定性采样/后验概率Key words
flow identification/ active learning/ Support Vector Machine(SVM)/ entropy/ uncertainty sample/ posteriori probability分类
信息技术与安全科学引用本文复制引用
刘三民,王彩霞,孙知信..一种基于SVM后验概率的网络流量识别方法[J].计算机工程,2012,38(17):171-173,3.基金项目
国家自然科学基金资助项目(60973140) （60973140）
江苏省自然科学基金资助项目(BK2009425) （BK2009425）
安徽省高等学校青年教师科研资助计划基金资助项目(2012SQRL220) （2012SQRL220）
