Efficient NP tests for anomaly detection over birth-death type DTMCs

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buir.contributor.authorKozat, Süleyman S.
dc.citation.epage184en_US
dc.citation.issueNumber2en_US
dc.citation.spage175en_US
dc.citation.volumeNumber90en_US
dc.contributor.authorÖzkan, H.en_US
dc.contributor.authorÖzkan, F.en_US
dc.contributor.authorDelibalta, I.en_US
dc.contributor.authorKozat, Süleyman S.en_US
dc.date.accessioned2019-02-21T16:10:43Z
dc.date.available2019-02-21T16:10:43Z
dc.date.issued2018en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractWe propose computationally highly efficient Neyman-Pearson (NP) tests for anomaly detection over birth-death type discrete time Markov chains. Instead of relying on extensive Monte Carlo simulations (as in the case of the baseline NP), we directly approximate the log-likelihood density to match the desired false alarm rate; and therefore obtain our efficient implementations. The proposed algorithms are appropriate for processing large scale data in online applications with real time false alarm rate controllability. Since we do not require parameter tuning, our algorithms are also adaptive to non-stationarity in the data source. In our experiments, the proposed tests demonstrate superior detection power compared to the baseline NP while nearly achieving the desired rates with negligible computational resources.
dc.description.provenanceMade available in DSpace on 2019-02-21T16:10:43Z (GMT). No. of bitstreams: 1 Bilkent-research-paper.pdf: 222869 bytes, checksum: 842af2b9bd649e7f548593affdbafbb3 (MD5) Previous issue date: 2018en
dc.description.sponsorshipAcknowledgments This work was supported in part by The Scientific and Technological Research Council of Turkey (TUBITAK) under Contract 113E517 and in part by Turk Telekom Inc.
dc.identifier.doi10.1007/s11265-016-1147-0
dc.identifier.issn1939-8018
dc.identifier.urihttp://hdl.handle.net/11693/50517
dc.language.isoEnglish
dc.publisherSpringer New York LLC
dc.relation.isversionofhttps://doi.org/10.1007/s11265-016-1147-0
dc.relation.project1.79769313486232E+308
dc.source.titleJournal of Signal Processing Systemsen_US
dc.subjectAnomaly detectionen_US
dc.subjectDTMCen_US
dc.subjectEfficienten_US
dc.subjectFalse alarmen_US
dc.subjectMarkoven_US
dc.subjectNeyman pearsonen_US
dc.subjectNPen_US
dc.subjectOnlineen_US
dc.titleEfficient NP tests for anomaly detection over birth-death type DTMCsen_US
dc.typeArticleen_US

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