Online anomaly detection using random forest

Zhiruo Zhao, Kishan G Mehrotra, Chilukuri K. Mohan

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

2 Scopus citations

Abstract

In this paper, we focus on how to use random forests based methods to improve the anomaly detection rate for streaming datasets. The key concept in a current work [12] is to build a random forest where in any tree, at any internal node, a feature is randomly selected and the associated data space is partitioned in half. However, the model parameters were pre-defined and the efficiency on applying this model for various conditions is not discussed. In this paper, we first give mathematical justification of required tree height and number of trees by casting the problem as a classical coupon collector problem. Then we design a majority voting score combination strategy to combine the results from different anomaly detection trees. Finally, we apply feature clustering to group the correlated features together in order to find the anomalies jointly determined by subsets of features.

Original languageEnglish (US)
Title of host publicationRecent Trends and Future Technology in Applied Intelligence - 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Proceedings
PublisherSpringer Verlag
Pages135-147
Number of pages13
ISBN (Print)9783319920573
DOIs
StatePublished - Jan 1 2018
Event31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems IEA/AIE 2018 - Montreal, Canada
Duration: Jun 25 2018Jun 28 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10868 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems IEA/AIE 2018
CountryCanada
CityMontreal
Period6/25/186/28/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Zhao, Z., Mehrotra, K. G., & Mohan, C. K. (2018). Online anomaly detection using random forest. In Recent Trends and Future Technology in Applied Intelligence - 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Proceedings (pp. 135-147). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10868 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-92058-0_13