Anomaly Detection

Anomaly Detection is a data analysis technique within the broader domain of pattern recognition that identifies rare or deviating instances in a dataset whose features significantly diverge from a defined norm, thereby distinguishing them from typical or expected patterns.

13.8K

Publications

885.3K

Citations

37.2K

Authors

5.3K

Institutions

Publications per year

2017–2026

8.4K

Authors

37.2K

  1. GP
    Guansong Pang

    Singapore Management University, Singapore

    40 publications · 4.1K citations

    29

    h-index

  2. QD
    Qian Du

    Mississippi State University, United States

    34 publications · 3.8K citations

    28

    h-index

  3. CL
    Christopher Leckie

    The University of Melbourne, Australia

    42 publications · 4K citations

    27

    h-index

  4. DP
    Dan Pei

    Tsinghua University, China

    32 publications · 4.5K citations

    27

    h-index

  5. SR
    Sutharshan Rajasegarar

    The University of Melbourne, Australia

    36 publications · 3.4K citations

    23

    h-index

1–5 of 37.2K

Venues

  1. IEEE Access

    418 publications · 31.2K citations

    88

    h-index

  2. arXiv (Cornell University)

    Repository · 396 publications · 19.7K citations

    66

    h-index

  3. Computers & Security

    124 publications · 14.8K citations

    59

    h-index

  4. Expert Systems with Applications

    166 publications · 14.5K citations

    59

    h-index

  5. IEEE Transactions on Geoscience and Remote Sensing

    128 publications · 12.8K citations

    56

    h-index

1–5