Quickest anomaly detection: A case of active hypothesis testing

Kobi Cohen, Qing Zhao

Research output: Contribution to conferencePaperpeer-review

9 Scopus citations

Abstract

The problem of quickest detection of an anomalous process among M processes is considered. At each time, a subset of the processes can be observed, and the observations follow two different distributions, depending on whether the process is normal or abnormal. The objective is a sequential search strategy that minimizes the expected detection time subject to an error probability constraint. This problem can be considered as a special case of active hypothesis testing first considered by Chernoff in 1959, where a randomized test was proposed and shown to be asymptotically optimal. For the special case considered in this paper, we show that a simple deterministic test achieves asymptotic optimality and offers better performance in the finite regime.

Original languageEnglish
DOIs
StatePublished - 1 Jan 2014
Externally publishedYes
Event2014 IEEE Information Theory and Applications Workshop, ITA 2014 - San Diego, CA, United States
Duration: 9 Feb 201414 Feb 2014

Conference

Conference2014 IEEE Information Theory and Applications Workshop, ITA 2014
Country/TerritoryUnited States
CitySan Diego, CA
Period9/02/1414/02/14

Keywords

  • Sequential detection
  • dynamic search
  • hypothesis testing

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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