A supervised clustering algorithm for computer intrusion detection
dc.contributor.author | Ye, Nong | en_US |
dc.contributor.author | Li, Xiangyang | en_US |
dc.date.accessioned | 2006-09-11T17:09:29Z | |
dc.date.available | 2006-09-11T17:09:29Z | |
dc.date.issued | 2005-11 | en_US |
dc.identifier.citation | Li, Xiangyang; Ye, Nong; (2005). "A supervised clustering algorithm for computer intrusion detection." Knowledge and Information Systems 8(4): 498-509. <http://hdl.handle.net/2027.42/45923> | en_US |
dc.identifier.issn | 0219-1377 | en_US |
dc.identifier.issn | 0219-3116 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/45923 | |
dc.description.abstract | We previously developed a clustering and classification algorithm—supervised (CCAS) to learn patterns of normal and intrusive activities and to classify observed system activities. Here we further enhance the robustness of CCAS to the presentation order of training data and the noises in training data. This robust CCAS adds data redistribution, a supervised hierarchical grouping of clusters and removal of outliers as the postprocessing steps. | en_US |
dc.format.extent | 2311546 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Springer-Verlag | en_US |
dc.subject.other | Classification | en_US |
dc.subject.other | Business Information Systems | en_US |
dc.subject.other | Intrusion Detection | en_US |
dc.subject.other | Computer Science | en_US |
dc.subject.other | Information Systems and Communication Service | en_US |
dc.subject.other | Clustering | en_US |
dc.title | A supervised clustering algorithm for computer intrusion detection | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Philosophy | en_US |
dc.subject.hlbsecondlevel | Computer Science | en_US |
dc.subject.hlbtoplevel | Humanities | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Industrial and Manufacturing Systems Engineering, University of Michigan—Dearborn, Dearborn, MI, 48128, USA | en_US |
dc.contributor.affiliationother | Department of Industrial Engineering, Arizona State University, Tempe, AZ, USA | en_US |
dc.contributor.affiliationumcampus | Dearborn | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/45923/1/10115_2005_Article_195.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/s10115-005-0195-8 | en_US |
dc.identifier.source | Knowledge and Information Systems | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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