Revisiting attribute independence assumption in probabilistic unsupervised anomaly detection

Sunil Aryal, Kai Ming Ting, Gholamreza Haffari

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

    4 Citations (Scopus)

    Abstract

    In this paper, we revisit the simple probabilistic approach of unsupervised anomaly detection by estimating multivariate probability as a product of univariate probabilities, assuming attributes are generated independently. We show that this simple traditional approach performs competitively to or better than five state-of-the-art unsupervised anomaly detection methods across a wide range of data sets from categorical, numeric or mixed domains. It is arguably the fastest anomaly detector. It is one order of magnitude faster than the fastest state-of-the- art method in high dimensional data sets.

    Original languageEnglish
    Title of host publicationIntelligence and Security Informatics
    Subtitle of host publication11th Pacific Asia Workshop, PAISI 2016, Auckland, New Zealand, April 19, 2016, Proceedings
    EditorsMichael Chau, G. Alan Wang, Hsinchun Chen
    Place of PublicationSwitzerland
    PublisherSpringer
    Pages73-86
    Number of pages14
    ISBN (Electronic)9783319318639
    ISBN (Print)9783319318622
    DOIs
    Publication statusPublished - 2016
    Event11th Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2016 - Auckland, New Zealand
    Duration: 19 Apr 201619 Apr 2016

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer
    Volume9650
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference11th Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2016
    CountryNew Zealand
    CityAuckland
    Period19/04/1619/04/16

    Keywords

    • Big data
    • Fast anomaly detection
    • Independence assumption

    Cite this

    Aryal, S., Ting, K. M., & Haffari, G. (2016). Revisiting attribute independence assumption in probabilistic unsupervised anomaly detection. In M. Chau, G. A. Wang, & H. Chen (Eds.), Intelligence and Security Informatics : 11th Pacific Asia Workshop, PAISI 2016, Auckland, New Zealand, April 19, 2016, Proceedings (pp. 73-86). (Lecture Notes in Computer Science ; Vol. 9650). Springer. https://doi.org/10.1007/978-3-319-31863-9_6