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An Effective Information Visualization Technique for Intrusion Detection: Hyperbolic View Intrusion Visualizer

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dc.contributor.author정윤석-
dc.contributor.author명노해-
dc.date.accessioned2021-09-07T17:38:45Z-
dc.date.available2021-09-07T17:38:45Z-
dc.date.created2021-06-17-
dc.date.issued2011-
dc.identifier.issn1229-1684-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/113723-
dc.description.abstractIn computer forensics investigation, the investigators collect, protect, analyze and interpret massive amount of data which were used in cyber crime. However, due to its huge amount of information, it takes a great deal of time and errors often result even when they use forensics investigation tool in the process. The information visualization techniques will greatly help to improve the information processing ability of human when they deal with the overwhelming amount of data and have to find out significant information in it. The importance of Intrusion Detection System(IDS) among network forensics is being emphasized in computer forensics. In this study, we apply the information visualization techniques which are proposed to be a great help to IDS and carry out the usability test to find out the most effective information visualization techniques for IDS.-
dc.languageKorean-
dc.language.isoko-
dc.publisher대한인간공학회-
dc.titleAn Effective Information Visualization Technique for Intrusion Detection: Hyperbolic View Intrusion Visualizer-
dc.title.alternativeAn Effective Information Visualization Technique for Intrusion Detection: Hyperbolic View Intrusion Visualizer-
dc.typeArticle-
dc.contributor.affiliatedAuthor명노해-
dc.identifier.bibliographicCitation대한인간공학회지, v.30, no.2, pp.319 - 330-
dc.relation.isPartOf대한인간공학회지-
dc.citation.title대한인간공학회지-
dc.citation.volume30-
dc.citation.number2-
dc.citation.startPage319-
dc.citation.endPage330-
dc.type.rimsART-
dc.identifier.kciidART001549517-
dc.description.journalClass2-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorComputer forensic-
dc.subject.keywordAuthorNetwork security-
dc.subject.keywordAuthorIntrusion detection-
dc.subject.keywordAuthorInformation visualization-
dc.subject.keywordAuthorFocus+Context-
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공과대학 (산업경영공학부)
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