Online anomaly detection under Markov statistics with controllable type-I error

dc.citation.epage1445en_US
dc.citation.issueNumber6en_US
dc.citation.spage1435en_US
dc.citation.volumeNumber64en_US
dc.contributor.authorOzkan, H.en_US
dc.contributor.authorOzkan, F.en_US
dc.contributor.authorKozat, S. S.en_US
dc.date.accessioned2018-04-12T10:42:25Z
dc.date.available2018-04-12T10:42:25Z
dc.date.issued2016en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractWe study anomaly detection for fast streaming temporal data with real time Type-I error, i.e., false alarm rate, controllability; and propose a computationally highly efficient online algorithm, which closely achieves a specified false alarm rate while maximizing the detection power. Regardless of whether the source is stationary or nonstationary, the proposed algorithm sequentially receives a time series and learns the nominal attributes - in the online setting - under possibly varying Markov statistics. Then, an anomaly is declared at a time instance, if the observations are statistically sufficiently deviant. Moreover, the proposed algorithm is remarkably versatile since it does not require parameter tuning to match the desired rates even in the case of strong nonstationarity. The presented study is the first to provide the online implementation of Neyman-Pearson (NP) characterization for the problem such that the NP optimality, i.e., maximum detection power at a specified false alarm rate, is nearly achieved in a truly online manner. In this regard, the proposed algorithm is highly novel and appropriate especially for the applications requiring sequential data processing at large scales/high rates due to its parameter-tuning free computational efficient design with the practical NP constraints under stationary or non-stationary source statistics. © 2015 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T10:42:25Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016en
dc.identifier.doi10.1109/TSP.2015.2504345en_US
dc.identifier.issn1053-587X
dc.identifier.urihttp://hdl.handle.net/11693/36499
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TSP.2015.2504345en_US
dc.source.titleIEEE Transactions on Signal Processingen_US
dc.subjectAnomaly detectionen_US
dc.subjectEfficienten_US
dc.subjectFalse alarmen_US
dc.subjectMarkoven_US
dc.subjectNeyman-Pearsonen_US
dc.subjectNPen_US
dc.subjectOnlineen_US
dc.subjectTime seriesen_US
dc.subjectType-I erroren_US
dc.titleOnline anomaly detection under Markov statistics with controllable type-I erroren_US
dc.typeArticleen_US

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