Searching for Unknown Anomalies in Hierarchical Data Streams

Tomer Gafni, Kobi Cohen, Qing Zhao

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

We consider the problem of anomaly detection among a large number of processes, where the probabilistic models of anomalies are unknown. At each time, aggregated noisy observations can be taken from a chosen subset of processes, where the chosen subset conforms to a tree structure. The observation distribution depends on the chosen subset and the absence/presence of anomalies. We develop a sequential search strategy using a hierarchical Kolmogorov-Smirnov (KS) statistics. Referred to as Tree-based Anomaly Search using KS statistics (TASKS), the proposed strategy is order-optimal with respect to the size of the search space and the detection accuracy.

Original languageEnglish
Pages (from-to)1774-1778
Number of pages5
JournalIEEE Signal Processing Letters
Volume28
DOIs
StatePublished - 1 Jan 2021

Keywords

  • Anomaly detection
  • dynamic search
  • sequential design of experiments

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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