Context-aware Anomaly Detection

Context-Aware Anomaly Detection is a methodological framework within anomaly detection that integrates situational and environmental context to dynamically adapt detection criteria, thereby distinguishing anomalies based on contextual relevance rather than static, context-independent thresholds.

129

Publications

6.9K

Citations

428

Authors

196

Institutions

Publications per year

2017–2026

79

Authors

428

  1. XW
    Xinyu Wu

    Shenzhen Institutes of Advanced Technology, China

    4 publications · 219 citations

    4

    h-index

  2. NL
    Nannan Li

    Macau University of Science and Technology, Macao SAR China

    4 publications · 219 citations

    4

    h-index

  3. PP
    Parvaneh Parvin

    University of Pisa, Italy

    3 publications · 45 citations

    3

    h-index

  4. UG
    Uwe Glässer

    Simon Fraser University, Canada

    3 publications · 90 citations

    3

    h-index

  5. HW
    Hans Wehn

    Simon Fraser University, Canada

    3 publications · 90 citations

    3

    h-index

1–5 of 428

Venues

  1. Procedia Computer Science

    3 publications · 60 citations

    3

    h-index

  2. IEEE Internet of Things Journal

    2 publications · 44 citations

    3

    h-index

  3. Neurocomputing

    3 publications · 195 citations

    3

    h-index

  4. 3

    h-index

  5. Pattern Recognition Letters

    3 publications · 126 citations

    3

    h-index

1–5