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

Authors

117

  1. SY
    Sungroh Yoon

    Seoul National University, South Korea

    2 publications · 169 citations

    2

    h-index

  2. SK
    Siwon Kim

    Seoul National University, South Korea

    2 publications · 169 citations

    2

    h-index

  3. KC
    Kukjin Choi

    Seoul National University, South Korea

    2 publications · 169 citations

    2

    h-index

  4. MA
    Mohd Aizaini Maarof

    University of Technology Malaysia, Malaysia

    2 publications · 27 citations

    2

    h-index

  5. HH
    Hailiang Huang

    Shanghai University of Finance and Economics, China

    2 publications · 267 citations

    2

    h-index

1–5 of 117

Venues

  1. IEEE Access

    3 publications · 111 citations

    3

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 26 citations

    2

    h-index

  3. International Journal of Sensor Networks

    2 publications · 27 citations

    2

    h-index

  4. 1

    h-index

  5. Computer Vision and Pattern Recognition

    Conference · 1 publications · 14 citations

    1

    h-index

1–5