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Article: A data reduction method for intrusion detection

TitleA data reduction method for intrusion detection
Authors
Issue Date1996
PublisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/jss
Citation
Journal Of Systems And Software, 1996, v. 33 n. 1, p. 101-108 How to Cite?
AbstractThis paper describes a technique for improving efficiency of data analysis involved in intrusion detection. Intrusion detection aims to detect security violations from abnormal patterns of system usage. It is required that user activities be monitored by the system and that monitoring data be analyzed to recognize behavior patterns of users. Multivariate data analysis may be used to achieve intrusion detection. Nevertheless, system monitoring typically records everything that each user performs in the system; hence, a massive volume of monitoring data is created. To allow analysis of monitoring data to be performed efficiently, it is essential to develop techniques that, without losing important information, reduce the amount of data to be processed. This paper presents a data reduction method that makes multivariate data analysis involved in intrusion detection more efficient. Our data reduction technique extracts, from the original data set, discriminating components that best characterize user behavior. This way, the amount of data to be processed by the multivariate data analysis module will be reduced substantially.
Persistent Identifierhttp://hdl.handle.net/10722/152256
ISSN
2015 Impact Factor: 1.424
2015 SCImago Journal Rankings: 0.897
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLam, KYen_US
dc.contributor.authorHui, Len_US
dc.contributor.authorChung, SLen_US
dc.date.accessioned2012-06-26T06:36:47Z-
dc.date.available2012-06-26T06:36:47Z-
dc.date.issued1996en_US
dc.identifier.citationJournal Of Systems And Software, 1996, v. 33 n. 1, p. 101-108en_US
dc.identifier.issn0164-1212en_US
dc.identifier.urihttp://hdl.handle.net/10722/152256-
dc.description.abstractThis paper describes a technique for improving efficiency of data analysis involved in intrusion detection. Intrusion detection aims to detect security violations from abnormal patterns of system usage. It is required that user activities be monitored by the system and that monitoring data be analyzed to recognize behavior patterns of users. Multivariate data analysis may be used to achieve intrusion detection. Nevertheless, system monitoring typically records everything that each user performs in the system; hence, a massive volume of monitoring data is created. To allow analysis of monitoring data to be performed efficiently, it is essential to develop techniques that, without losing important information, reduce the amount of data to be processed. This paper presents a data reduction method that makes multivariate data analysis involved in intrusion detection more efficient. Our data reduction technique extracts, from the original data set, discriminating components that best characterize user behavior. This way, the amount of data to be processed by the multivariate data analysis module will be reduced substantially.en_US
dc.languageengen_US
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/jssen_US
dc.relation.ispartofJournal of Systems and Softwareen_US
dc.titleA data reduction method for intrusion detectionen_US
dc.typeArticleen_US
dc.identifier.emailHui, L:hui@cs.hku.hken_US
dc.identifier.authorityHui, L=rp00120en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/0164-1212(95)00106-9en_US
dc.identifier.scopuseid_2-s2.0-0030129318en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0030129318&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume33en_US
dc.identifier.issue1en_US
dc.identifier.spage101en_US
dc.identifier.epage108en_US
dc.identifier.isiWOS:A1996UC08000009-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridLam, KY=7403657062en_US
dc.identifier.scopusauthoridHui, L=8905728300en_US
dc.identifier.scopusauthoridChung, SL=7404292662en_US

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