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dc.contributor.authorWeng, Fangfeizh_CN
dc.contributor.authorJiang, Qingshanzh_CN
dc.contributor.authorShi, Liangzh_CN
dc.contributor.authorWu, Nannanzh_CN
dc.contributor.author姜青山zh_CN
dc.contributor.author史亮zh_CN
dc.date.accessioned2015-07-22T02:16:08Z
dc.date.available2015-07-22T02:16:08Z
dc.date.issued2007zh_CN
dc.identifier.citation2007 International Workshop on Anti-counterfeiting, Security, and Identification, 2007:121-124zh_CN
dc.identifier.otherWOS:000250042400029zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/85722
dc.descriptionConference Name:International Workshop on Anti-counterfeiting, Security, and Identification. Conference Address: Xiamen, PEOPLES R CHINA. Time:APR 16-18, 2007.zh_CN
dc.description.abstractIntrusion detection system (IDS) is an important component of computer network security, while clustering analysis is a common unsupervised anomaly detection method. However, it is difficult for the single clustering algorithm to get the great effective detection, and the data of intrusion attacks is anomalistic normally. This paper presents an unsupervised anomaly detection system based on the clustering ensemble. The system is based on the multiple runs of K-means to accumulate evidence to avoid the false classification of anomalistic data; then using single-link to construct the hierarchical clustering tree to get the ultimate clustering result to solve the above problems. Finally, the KDD99 CUP test data is used to show that this system is greatly effective. It also compares with another IDS based on congeneric clustering algorithm to demonstrate the superiority of this system.zh_CN
dc.language.isoen_USzh_CN
dc.publisherIEEEzh_CN
dc.source.urihttp://dx.doi.org/10.1109/IWASID.2007.373710zh_CN
dc.titleAn intrusion detection system based on the clustering ensemblezh_CN
dc.typeConferencezh_CN


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