Anomaly Detection Validation
Anomaly Detection Validation is a methodological framework within signal verification that systematically assesses the correctness, reliability, and generalization performance of anomaly detection models by distinguishing genuine anomalies from false positives through rigorously defined ground-truth benchmarks, statistical tests, and contextual coherence criteria.
31
Publications
1.7K
Citations
117
Authors
58
Institutions
Publications per year
2017–2026
18
Parents
31
Comparing anomaly-detection algorithms for keystroke dynamics
Kevin S. Killourhy, Roy A. Maxion · 2009 · 545 citations
ADBench: Anomaly Detection Benchmark
Songqiao Han, Xiyang Hu, Hailiang Huang et al. · SSRN Electronic Journal · 2022 · 252 citations · Full text
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Arizona State University
Tempe, United States
2 h-index
3 publications
318 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
4 publications
1.6K citations
Taiz University
Ta‘izz, Yemen
2 h-index
2 publications
27 citations
University of Technology Malaysia
Johor Bahru, Malaysia
2 h-index
4 publications
54 citations

Masaryk University
Brno, Czechia
1 h-index
4 publications
88 citations

Bangladesh University of Engineering and Technology
Dhaka, Bangladesh
1 h-index
3 publications
72 citations
Shanghai University of Finance and Economics
Shanghai, China
1 h-index
1 publications
252 citations
Huawei Technologies (United States)
Plano, United States
1 h-index
2 publications
44 citations
1–8 of 58
3 publications · 111 citations
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Repository · 2 publications · 26 citations
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2 publications · 27 citations
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1 publications · 21 citations
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Conference · 1 publications · 14 citations
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