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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17745
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dc.contributor.authorYao, H-
dc.contributor.authorWang, Q-
dc.contributor.authorWang, L-
dc.contributor.authorZhang, P-
dc.contributor.authorLi, M-
dc.contributor.authorLiu, Y-
dc.date.accessioned2019-03-20T13:41:08Z-
dc.date.available2017-10-31-
dc.date.available2019-03-20T13:41:08Z-
dc.date.issued2017-10-25-
dc.identifier.citationInternational Journal of Parallel Programming, 2017, pp. 1 - 19en_US
dc.identifier.issn0885-7458-
dc.identifier.issnhttp://dx.doi.org/10.1007/s10766-017-0537-7-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/17745-
dc.format.extent1 - 19-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectIntrusion Detection Systemen_US
dc.subjectMulti-Levelen_US
dc.subjectMachine Learningen_US
dc.subjectData Engineeringen_US
dc.subjectKDDCUP99en_US
dc.titleAn Intrusion Detection Framework Based on Hybrid Multi-Level Data Miningen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1007/s10766-017-0537-7-
dc.relation.isPartOfInternational Journal of Parallel Programming-
pubs.publication-statusAccepted-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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