Searching for Anomalies over Composite Hypotheses

Bar Hemo, Tomer Gafni, Kobi Cohen, Qing Zhao

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The problem of detecting anomalies in multiple processes is considered. We consider a composite hypothesis case, in which the measurements drawn when observing a process follow a common distribution with an unknown parameter (vector), whose value lies in normal or abnormal parameter spaces, depending on its state. The objective is a sequential search strategy that minimizes the expected detection time subject to an error probability constraint. We develop a deterministic search algorithm with the following desired properties. First, when no additional side information on the process states is known, the proposed algorithm is asymptotically optimal in terms of minimizing the detection delay as the error probability approaches zero. Second, when the parameter value under the null hypothesis is known and equal for all normal processes, the proposed algorithm is asymptotically optimal as well, with better detection time determined by the true null state. Third, when the parameter value under the null hypothesis is unknown, but is known to be equal for all normal processes, the proposed algorithm is consistent in terms of achieving error probability that decays to zero with the detection delay. Finally, an explicit upper bound on the error probability under the proposed algorithm is established for the finite sample regime. Extensive experiments on synthetic dataset and DARPA intrusion detection dataset are conducted, demonstrating strong performance of the proposed algorithm over existing methods.

Original languageEnglish
Article number8981802
Pages (from-to)1181-1196
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume68
DOIs
StatePublished - 1 Jan 2020

Keywords

  • Anomaly detection
  • dynamic search
  • sequential design of experiments

Fingerprint

Dive into the research topics of 'Searching for Anomalies over Composite Hypotheses'. Together they form a unique fingerprint.

Cite this