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dc.contributor.authorWang, J.en_US
dc.contributor.authorRossell, D.en_US
dc.contributor.authorCassandras, C. G.en_US
dc.contributor.authorPaschalidis, Ioannis Ch.en_US
dc.date.accessioned2016-09-30T02:05:40Z
dc.date.available2016-09-30T02:05:40Z
dc.date.issued2013-12
dc.identifier.citationJ Wang, D Rossell, CG Cassandras, I Ch Paschalidis. 2013. "Network Anomaly Detection: A Survey and Comparative Analysis of Stochastic and Deterministic Methods." Proceedings of the 52nd IEEE Conference on Decision and Control, pp. 182 - 187.
dc.identifier.otherhttps://arxiv.org/abs/1309.4844
dc.identifier.urihttps://hdl.handle.net/2144/18027
dc.description7 pages. 1 more figure than final CDC 2013 versionen_US
dc.description.abstractWe present five methods to the problem of network anomaly detection. These methods cover most of the common techniques in the anomaly detection field, including Statistical Hypothesis Tests (SHT), Support Vector Machines (SVM) and clustering analysis. We evaluate all methods in a simulated network that consists of nominal data, three flow-level anomalies and one packet-level attack. Through analyzing the results, we point out the advantages and disadvantages of each method and conclude that combining the results of the individual methods can yield improved anomaly detection results.en_US
dc.format.extentp. 182 - 187en_US
dc.language.isoen_US
dc.relation.ispartofProceedings of the 52nd IEEE Conference on Decision and Control
dc.relation.ispartofseriesDecision and Control (CDC), 2013 IEEE 52nd Annual Conference on;
dc.rightsAttribution 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectMachine learningen_US
dc.subjectNetworking and internet architectureen_US
dc.titleNetwork anomaly detection: a survey and comparative analysis of stochastic and deterministic methodsen_US
dc.typeArticleen_US
dc.typeConference materialsen_US
dc.identifier.doi10.1109/CDC.2013.6759879
pubs.notesEmbargo: No embargoen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University/College of Engineeringen_US
pubs.organisational-groupBoston University/College of Engineering/Department of Electrical & Computer Engineeringen_US


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International