Non-Gaussian Models for Context Aware Anomaly Detection

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dc.contributor.advisorTamir, Dan
dc.contributor.authorLakomski, Gregory Randall
dc.contributor.committeeMemberGuirguis, Mina
dc.contributor.committeeMemberEkin, Tahir
dc.date.accessioned2017-05-16T13:27:38Z
dc.date.available2017-05-16T13:27:38Z
dc.date.issued2017-05
dc.description.abstractNo abstract prepared.
dc.description.departmentComputer Science
dc.formatText
dc.format.extent81 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationLakomski, G. R. (2017). <i>Non-Gaussian models for context aware anomaly detection</i> (Unpublished thesis). Texas State University, San Marcos, Texas.
dc.identifier.urihttps://hdl.handle.net/10877/6571
dc.language.isoen
dc.subjectNon-Gaussian
dc.subjectContext
dc.subjectAnomaly
dc.subject.lcshComputer networks--Monitoringen_US
dc.subject.lcshGaussian processesen_US
dc.titleNon-Gaussian Models for Context Aware Anomaly Detection
dc.typeThesis
thesis.degree.departmentComputer Science
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorTexas State Universityen_US
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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