Show simple item record

dc.contributor.authorZhang, Jingen_US
dc.contributor.authorPaschalidis, Ioannis Ch.en_US
dc.date.accessioned2016-08-26T01:29:51Z
dc.date.accessioned2016-09-29T19:28:42Z
dc.date.available2016-09-29T19:28:42Z
dc.date.issued2015-12
dc.identifier.citationJing Zhang, I Ch Paschalidis. 2015. "An Improved Composite Hypothesis Test for Markov Models with Applications in Network Anomaly Detection." Proceedings of the 54th IEEE Conference on Decision and Control, pp. 3810 - 3815.
dc.identifier.otherhttp://arxiv.org/abs/1509.01706v2
dc.identifier.urihttps://hdl.handle.net/2144/18021
dc.description.abstractRecent work has proposed the use of a composite hypothesis Hoeffding test for statistical anomaly detection. Setting an appropriate threshold for the test given a desired false alarm probability involves approximating the false alarm probability. To that end, a large deviations asymptotic is typically used which, however, often results in an inaccurate setting of the threshold, especially for relatively small sample sizes. This, in turn, results in an anomaly detection test that does not control well for false alarms. In this paper, we develop a tighter approximation using the Central Limit Theorem (CLT) under Markovian assumptions. We apply our result to a network anomaly detection application and demonstrate its advantages over earlier work.en_US
dc.format.extent3810 - 3815en_US
dc.language.isoen_US
dc.relation.ispartofProceedings of the 54th IEEE Conference on Decision and Control
dc.relation.ispartofseriesProceedings of the 54th IEEE Conference on Decision and Control
dc.relation.replaceshttp://hdl.handle.net/2144/17757
dc.relation.replaces2144/17757
dc.rightsAttribution 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectMarkov processesen_US
dc.subjectModelingen_US
dc.subjectTaylor seriesen_US
dc.subjectHoeffding testen_US
dc.subjectProbabilityen_US
dc.subjectConvergenceen_US
dc.titleAn Improved Composite Hypothesis Test for Markov Models with Applications in Network Anomaly Detectionen_US
dc.typeArticleen_US
dc.typeConference materialsen_US
dc.identifier.doi10.1109/CDC.2015.7402811
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


This item appears in the following Collection(s)

Show simple item record

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