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Detecting Network Anomalies in Mixed-Attribute Data Sets

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Tran, Khoi-Nguyen; Jin, Huidong (Warren)

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Detecting network anomalies is important part of intrusion detection systems that have been developed with great successes on homogeneous data. There have been successes with mixed-attribute data using various techniques, however, few of them exist for using mixed-attribute data without further manipulation or consideration of dependencies among the different types of attributes. We propose in this paper a fusion of decision tree and Gaussian mixture model (GMM) to detect anomalies in...[Show more]

dc.contributor.authorTran, Khoi-Nguyen
dc.contributor.authorJin, Huidong (Warren)
dc.coverage.spatialPhuket Thailand
dc.date.accessioned2015-12-10T22:39:27Z
dc.date.createdJanuary 9-10 2010
dc.identifier.isbn9780769539232
dc.identifier.urihttp://hdl.handle.net/1885/57182
dc.description.abstractDetecting network anomalies is important part of intrusion detection systems that have been developed with great successes on homogeneous data. There have been successes with mixed-attribute data using various techniques, however, few of them exist for using mixed-attribute data without further manipulation or consideration of dependencies among the different types of attributes. We propose in this paper a fusion of decision tree and Gaussian mixture model (GMM) to detect anomalies in mixed-attribute data sets. Evaluation experiments were performed on the popular KDDCup 1999 data set using C4.5 decision tree, GMM and the fusion of C4.5 and GMM.
dc.publisherIEEE Computer Society
dc.relation.ispartofseriesInternational Conference on Knowledge Discovery and Data Mining (WKDD 2010)
dc.sourceProceedings of the Third International Conference on Knowledge Discovery and Data Mining
dc.source.urihttp://www.informatik.uni-trier.de/~ley/db/conf/wkdd/wkdd2010.html#TranJ10
dc.subjectKeywords: Anomaly detection; Attribute data; Data sets; Evaluation experiments; Gaussian Mixture Model; Intrusion Detection Systems; Network anomalies; Computer crime; Data mining; Decision trees; Magnetostrictive devices; Measurement theory; Intrusion detection Anomaly detection; C4.5 decision tree; Gaussian mixture model
dc.titleDetecting Network Anomalies in Mixed-Attribute Data Sets
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2010
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationU3594520xPUB390
local.type.statusPublished Version
local.contributor.affiliationTran, Khoi-Nguyen, College of Engineering and Computer Science, ANU
local.contributor.affiliationJin, Huidong (Warren), College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage383
local.bibliographicCitation.lastpage386
local.identifier.doi10.1109/WKDD.2010.96
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
dc.date.updated2016-02-24T10:18:53Z
local.identifier.scopusID2-s2.0-77952117886
CollectionsANU Research Publications

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