Autonomous anomaly detection

Xiaowei Gu, Plamen Parvanov Angelov

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

16 Dyfyniadau(SciVal)

Crynodeb

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.
Iaith wreiddiolSaesneg
TeitlIEEE Conference on Evolving and Adaptive Intelligent Systems 2017
Is-deitlConference Proceedings
GolygyddionIgor Skrjanc, Saso Blazic
CyhoeddwrIEEE Press
ISBN (Argraffiad)9781509064441
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsE-gyhoeddi cyn argraffu - 22 Meh 2017
Cyhoeddwyd yn allanolIe

Cyfres gyhoeddiadau

EnwIEEE Conference on Evolving and Adaptive Intelligent Systems
Cyfrol2017-May
ISSN (Argraffiad)2330-4863
ISSN (Electronig)2473-4691

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