文献类型：会议论文

英文题名：Support vector machines for anomaly detection

作者：Zhang, Xueqin[1];Gu, Chunhua[1];Lin, Jiajun[1]

机构：[1]East China Univ Sci & Technol, Coll Informat Sci & Engn, Shanghai 200237, Peoples R China

会议论文集：6th World Congress on Intelligent Control and Automation

会议日期：JUN 21-23, 2006

会议地点：Dalian, PEOPLES R CHINA

语种：英文

外文关键词：intrusion detection; Windows Registry; support vector machines; feature representation

摘要：The support vector machines is a widely used tool for classification. In this paper, firstly the method of selected features of Windows Registry access recorder to construct detection data set was discussed and two kinds of feature representation methods adapted to SVM algorithm was described. Secondly, the algorithms of standard SVM that are used to classification was presented. At last, we implemented the standard SVM algorithm, weighted SVM and one class SVM to build models for different kind of data set. Experiment results on test data are given to illustrate the performance of these models. It is found that the C-SVM has high detection precision to predict the known examples and can also detect some unknown examples. Weighted SVM can effectively solve the misclassification problem resulted from the unbalance data set, one class SVM is an effective way to deal with unsupervised data.