Efficient NP tests for anomaly detection over birth-death type DTMCs
buir.contributor.author | Kozat, Süleyman S. | |
dc.citation.epage | 184 | en_US |
dc.citation.issueNumber | 2 | en_US |
dc.citation.spage | 175 | en_US |
dc.citation.volumeNumber | 90 | en_US |
dc.contributor.author | Özkan, H. | en_US |
dc.contributor.author | Özkan, F. | en_US |
dc.contributor.author | Delibalta, I. | en_US |
dc.contributor.author | Kozat, Süleyman S. | en_US |
dc.date.accessioned | 2019-02-21T16:10:43Z | |
dc.date.available | 2019-02-21T16:10:43Z | |
dc.date.issued | 2018 | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description.abstract | We 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.provenance | Made 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: 2018 | en |
dc.description.sponsorship | Acknowledgments 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.doi | 10.1007/s11265-016-1147-0 | |
dc.identifier.issn | 1939-8018 | |
dc.identifier.uri | http://hdl.handle.net/11693/50517 | |
dc.language.iso | English | |
dc.publisher | Springer New York LLC | |
dc.relation.isversionof | https://doi.org/10.1007/s11265-016-1147-0 | |
dc.relation.project | 1.79769313486232E+308 | |
dc.source.title | Journal of Signal Processing Systems | en_US |
dc.subject | Anomaly detection | en_US |
dc.subject | DTMC | en_US |
dc.subject | Efficient | en_US |
dc.subject | False alarm | en_US |
dc.subject | Markov | en_US |
dc.subject | Neyman pearson | en_US |
dc.subject | NP | en_US |
dc.subject | Online | en_US |
dc.title | Efficient NP tests for anomaly detection over birth-death type DTMCs | en_US |
dc.type | Article | en_US |
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