Universal outlier hypothesis testing: Application to anomaly detection

Yun Li, Sirin Nitinawarat, Yu Su, Venugopal Varadachari Veeravalli

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

Abstract

In outlier hypothesis testing, multiple observation sequences are collected, a small subset of which are outliers. Observations in an outlier sequence are generated by a mechanism different from that generating the observations in the majority of sequences. The goal is to best discern all the outlier sequences without any knowledge of the underlying generating mechanisms. A generalized likelihood test is considered in the fixed sample size setting. In the sequential setting, a test based on the Multihypothesis Sequential Probability Ratio Test and the repeated significance test is considered. The sequential test outperforms the generalized likelihood test when the lengths of the observation sequences exceed certain values. Applied to a real data set for spam detection, the performance of the proposed tests is shown to be superior to those based on the maximum mean discrepancy for large sample size.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5595-5599
Number of pages5
ISBN (Electronic)9781467369978
DOIs
StatePublished - Aug 4 2015
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: Apr 19 2014Apr 24 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2015-August
ISSN (Print)1520-6149

Other

Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
CountryAustralia
CityBrisbane
Period4/19/144/24/14

Keywords

  • anomaly detection
  • generalized likelihood test
  • maximum mean discrepancy
  • multihypothesis sequential probability ratio test
  • universal outlier hypothesis testing

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Li, Y., Nitinawarat, S., Su, Y., & Veeravalli, V. V. (2015). Universal outlier hypothesis testing: Application to anomaly detection. In 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings (pp. 5595-5599). [7179042] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2015-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2015.7179042