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dc.contributor.authorMcGregor, Anthony Jamesen_NZ
dc.contributor.authorHall, Mark A.en_NZ
dc.contributor.authorLorier, Perryen_NZ
dc.contributor.authorBrunskill, Jamesen_NZ
dc.contributor.editorBarakat, Cen_NZ
dc.contributor.editorPratt, Ien_NZ
dc.coverage.spatialUniv Cambridge, Juan les Pins, FRANCEen_NZ
dc.date.accessioned2017-01-22T23:06:31Z
dc.date.available2004en_NZ
dc.date.available2017-01-22T23:06:31Z
dc.date.issued2004en_NZ
dc.identifier.citationMcGregor, A. J., Hall, M. A., Lorier, P., & Brunskill, J. (2004). Flow clustering using machine learning techniques. In C. Barakat & I. Pratt (Eds.), Passive and Active Network Measurement. PAM 2004. Lecture Notes in Computer Science (Vol. 3015, pp. 205–214). Springer, Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-24668-8_21en
dc.identifier.isbn3-540-21492-5en_NZ
dc.identifier.issn0302-9743en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/10848
dc.description.abstractPacket header traces are widely used in network analysis. Header traces are the aggregate of traffic from many concurrent applications. We present a methodology, based on machine learning, that can break the trace down into clusters of traffic where each cluster has different traffic characteristics. Typical clusters include bulk transfer, single and multiple transactions and interactive traffic, amongst others. The paper includes a description of the methodology, a visualisation of the attribute statistics that aids in recognising cluster types and a discussion of the stability and effectiveness of the methodology.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringeren_NZ
dc.rightsThis is an author’s accepted version of an article published in Passive and Active Network Measurement. PAM 2004. Lecture Notes in Computer Science, LNCS 3015. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-24668-8_21. © Springer, Berlin, Heidelberg 2004
dc.source5th International Workshop on Passive and Active Network Measurementen_NZ
dc.subjectScience & Technologyen_NZ
dc.subjectTechnologyen_NZ
dc.subjectComputer Science, Information Systemsen_NZ
dc.subjectComputer Science, Theory & Methodsen_NZ
dc.subjectComputer Scienceen_NZ
dc.titleFlow clustering using machine learning techniquesen_NZ
dc.typeConference Contribution
dc.identifier.doi10.1007/978-3-540-24668-8_21
dc.relation.isPartOfPassive and Active Network Measurement. PAM 2004. Lecture Notes in Computer Scienceen_NZ
pubs.begin-page205
pubs.elements-id14825
pubs.end-page214
pubs.finish-date2004-04-20en_NZ
pubs.place-of-publicationSpringer, Berlin, Heidelberg
pubs.publication-statusPublisheden_NZ
pubs.start-date2004-04-19en_NZ
pubs.volume3015en_NZ


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