Autonomous anomaly detection

Xiaowei Gu, Plamen Parvanov Angelov

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

15 Citations (SciVal)

Abstract

In this paper, a new approach for autonomous anomaly detection is introduced within the Empirical Data Analytics (EDA) framework. This approach is fully data-driven and free from thresholds. Employing the nonparametric EDA estimators, the proposed approach can autonomously detect anomalies in an objective way based on the mutual distribution and ensemble properties of the data. The proposed approach firstly identifies the potential anomalies based on two EDA criteria, and then, partitions them into shape-free, non-parametric data clouds. Finally, it identifies the anomalies in regards to each data cloud (locally). Numerical examples based on synthetic and benchmark datasets demonstrate the validity and efficiency of the proposed approach.
Original languageEnglish
Title of host publicationIEEE Conference on Evolving and Adaptive Intelligent Systems 2017
Subtitle of host publicationConference Proceedings
EditorsIgor Skrjanc, Saso Blazic
PublisherIEEE Press
ISBN (Print)9781509064441
DOIs
Publication statusE-pub ahead of print - 22 Jun 2017
Externally publishedYes

Publication series

NameIEEE Conference on Evolving and Adaptive Intelligent Systems
Volume2017-May
ISSN (Print)2330-4863
ISSN (Electronic)2473-4691

Keywords

  • Empirical Data Analytics (EDA)
  • autonomous anomaly detection
  • data cloud
  • nonparametric

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