Anomaly Detection Thresholds
Anomaly Detection Thresholds is a decision boundary mechanism in anomaly detection systems that distinguishes normal from anomalous data instances by defining the critical value beyond which a score or metric is classified as indicative of an anomaly.
29
Publications
940
Citations
120
Authors
63
Institutions
Publications per year
2017–2026
17
Parents
29
Robust and Rapid Adaption for Concept Drift in Software System Anomaly Detection
Minghua Ma, Shenglin Zhang, Dan Pei et al. · 2018 · 87 citations
Wang‐Ji Yan, Dimitrios Chronopoulos, Ka‐Veng Yuen et al. · Mechanical Systems and Signal Processing · 2021 · 76 citations
Engineering, Civil Engineering, Computational Engineering +17
1–5 of 29
120
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University of North Carolina at Charlotte, United States
1 publications · 27 citations
1
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1
h-index
1
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1
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1–5 of 120
63

University of North Carolina at Charlotte
Charlotte, United States
2 h-index
3 publications
91 citations
RMIT University
Melbourne, Australia
2 h-index
3 publications
117 citations
Guangdong Pharmaceutical University
Guangzhou, China
1 h-index
1 publications
29 citations
Georgia Institute of Technology
Atlanta, United States
1 h-index
3 publications
75 citations

Shanghai Jiao Tong University
Shanghai, China
1 h-index
4 publications
128 citations
Center for Information Technology
Bethesda, United States
1 h-index
2 publications
174 citations

United States Department of Transportation
Washington, United States
1 h-index
1 publications
37 citations
ARC Centre of Excellence for Mathematical and Statistical Frontiers
Parkville, Australia
1 h-index
3 publications
195 citations
1–8 of 63
2 publications · 59 citations
2
h-index
2 publications · 47 citations
2
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1 publications · 15 citations
1
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TISSEC · 1 publications · 27 citations
1
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1 publications · 13 citations
1
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1–5