Anomaly Classification
Anomaly Classification is a methodological framework within incident classification that identifies and categorizes deviations from normal behavior or expected patterns by distinguishing anomalous events from baseline or nominal conditions.
299
Publications
14.8K
Citations
1.1K
Authors
440
Institutions
Publications per year
2017–2026
195
Parents
299
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
Jongheon Jeong, Taewan Kim, Dongqing Zhang et al. · 2023 · 363 citations
Bayesian event classification for intrusion detection
Christopher Kruegel, D. Mutz, William Robertson et al. · 2004 · 347 citations
1–5 of 299
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440
Tsinghua University
Beijing, China
5 h-index
20 publications
2.8K citations

Shanghai Jiao Tong University
Shanghai, China
5 h-index
14 publications
513 citations
University of Michigan
Ann Arbor, United States
4 h-index
11 publications
628 citations
Georgia Institute of Technology
Atlanta, United States
4 h-index
15 publications
623 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
3 h-index
5 publications
376 citations
Amirkabir University of Technology
Tehran, Iran
3 h-index
7 publications
215 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
8 publications
265 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
6 publications
442 citations
1–8 of 440
Repository · 13 publications · 413 citations
11
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9 publications · 390 citations
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4 publications · 262 citations
4
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3 publications · 115 citations
3
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3 publications · 308 citations
3
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