usfAD: a robust anomaly detector based on unsupervised stochastic forest

Aryal, Sunil, Santosh, KC and Dazeley, Richard 2020, usfAD: a robust anomaly detector based on unsupervised stochastic forest, International journal of machine learning and cybernetics, pp. 1-14, doi: 10.1007/s13042-020-01225-0.

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Title usfAD: a robust anomaly detector based on unsupervised stochastic forest
Author(s) Aryal, SunilORCID iD for Aryal, Sunil orcid.org/0000-0002-6639-6824
Santosh, KC
Dazeley, RichardORCID iD for Dazeley, Richard orcid.org/0000-0002-6199-9685
Journal name International journal of machine learning and cybernetics
Start page 1
End page 14
Total pages 14
Publisher Springer
Place of publication Cham, Switzerland
Publication date 2020-11-02
ISSN 1868-8071
1868-808X
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
Measurement scales and units
Anomaly detection
Outlier detection
Robust anomaly detection
Intrusion detection
Spam detection
Cyber security
Language eng
DOI 10.1007/s13042-020-01225-0
Indigenous content off
Field of Research 0801 Artificial Intelligence and Image Processing
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30145270

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Created: Thu, 12 Nov 2020, 11:56:02 EST

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