Sequential anomaly detection with observation control

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The problem of anomaly detection is considered when multiple processes are observed sequentially, but it is possible to sample only a subset of them at a time according to an adaptive sampling policy. The problem is to stop sampling as soon as possible and identify the anomalous processes, while controlling appropriate error probabilities. We consider two versions of this problem: in the first one there is no assumption regarding the anomalous processes, in the second their number is assumed to be known a priori. For each version, we obtain the optimal asymptotic performance as the error probabilities vanish and characterize the sampling rules that lead to asymptotic optimality. Moreover, we present two sampling rules for each setup, which differ in terms of the computational complexity and the actual performance they imply.

Original languageEnglish (US)
Title of host publication2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2389-2393
Number of pages5
ISBN (Electronic)9781538692912
DOIs
StatePublished - Jul 2019
Event2019 IEEE International Symposium on Information Theory, ISIT 2019 - Paris, France
Duration: Jul 7 2019Jul 12 2019

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2019-July
ISSN (Print)2157-8095

Conference

Conference2019 IEEE International Symposium on Information Theory, ISIT 2019
CountryFrance
CityParis
Period7/7/197/12/19

Keywords

  • Anomaly detection
  • asymptotic optimality
  • outlying sequence detection
  • sequential design of experiments

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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