Active Anomaly Detection in Heterogeneous Processes

Boshuang Huang, Kobi Cohen, Qing Zhao

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

An active inference problem of detecting anomalies among heterogeneous processes is considered. At each time, a subset of processes can be probed. The objective is to design a sequential probing strategy that dynamically determines which processes to observe at each time and when to terminate the search so that the expected detection time is minimized under a constraint on the probability of misclassifying any process. This problem falls into the general setting of sequential design of experiments pioneered by Chernoff in 1959, in which a randomized strategy, referred to as the Chernoff test, was proposed and shown to be asymptotically optimal as the error probability approaches zero. For the problem considered in this paper, a low-complexity deterministic test is shown to enjoy the same asymptotic optimality while offering significantly better performance in the finite regime and faster convergence to the optimal rate function, especially when the number of processes is large. Furthermore, the proposed test offers considerable reduction in computation complexity.

Original languageEnglish
Article number8443436
Pages (from-to)2284-2301
Number of pages18
JournalIEEE Transactions on Information Theory
Volume65
Issue number4
DOIs
StatePublished - 1 Apr 2019

Keywords

  • Active hypothesis testing
  • anomaly detection
  • dynamic search
  • sequential design of experiments
  • target whereabout

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

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