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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/111877
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dc.contributor.authorXia, H.-
dc.contributor.authorFang, B.-
dc.contributor.authorRoughan, M.-
dc.contributor.authorCho, K.-
dc.contributor.authorTune, P.-
dc.date.issued2018-
dc.identifier.citationComputer Networks, 2018; 135:15-31-
dc.identifier.issn1389-1286-
dc.identifier.issn1872-7069-
dc.identifier.urihttp://hdl.handle.net/2440/111877-
dc.description.abstractAbstract not available-
dc.description.statementofresponsibilityHui Xia, Bin Fang, Matthew Roughan, Kenjiro Cho, Paul Tune-
dc.language.isoen-
dc.publisherElsevier-
dc.rights©2018 Elsevier B.V. All rights reserved-
dc.source.urihttp://dx.doi.org/10.1016/j.comnet.2018.01.025-
dc.subjectAnomaly detection; basis; evolution; low false-alarm probability; SVD-
dc.titleA BasisEvolution framework for network traffic anomaly detection-
dc.typeJournal article-
dc.identifier.doi10.1016/j.comnet.2018.01.025-
dc.relation.granthttp://purl.org/au-research/grants/arc/DP110103505-
dc.relation.granthttp://purl.org/au-research/grants/arc/CE140100049-
pubs.publication-statusPublished-
dc.identifier.orcidRoughan, M. [0000-0002-7882-7329]-
Appears in Collections:Aurora harvest 3
Computer Science publications

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