Anomaly Detection Benchmarking
Anomaly Detection Benchmarking is a systematic evaluation framework that standardizes the comparative assessment of anomaly detection methods in time series by defining consistent datasets, performance metrics, and experimental protocols to enable objective validation and reproducibility across studies.
104
Publications
9.9K
Citations
462
Authors
180
Institutions
Publications per year
2017–2026
83
104
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines
Kukjin Choi, Jihun Yi, Changhwa Park et al. · IEEE Access · 2021 · 547 citations · Full text
Comparing anomaly-detection algorithms for keystroke dynamics
Kevin S. Killourhy, Roy A. Maxion · 2009 · 545 citations
1–5 of 104
462
4
h-index
4
h-index
4
h-index
4
h-index
3
h-index
1–5 of 462
180
Université Paris Cité
Paris, France
4 h-index
7 publications
377 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
7 publications
1.9K citations
The Ohio State University
Columbus, United States
3 h-index
4 publications
126 citations
École Polytechnique
Palaiseau, France
3 h-index
11 publications
432 citations

UNSW Sydney
Sydney, Australia
3 h-index
8 publications
688 citations
University of Chicago
Chicago, United States
3 h-index
11 publications
591 citations
Oregon State University
Corvallis, United States
3 h-index
12 publications
810 citations
Instituto de Física Corpuscular
Paterna, Spain
3 h-index
3 publications
113 citations
1–8 of 180
Repository · 19 publications · 1K citations
14
h-index
PVLDB · 9 publications · 382 citations
9
h-index
2 publications · 1.4K citations
2
h-index
2 publications · 604 citations
2
h-index
2 publications · 98 citations
2
h-index
1–5