Threat Data Fusion

Threat Data Fusion is a systematic process in threat intelligence integration that combines heterogeneous threat data from multiple sources to produce enriched, consistent, and actionable cyber threat insights, distinguished by its ability to resolve discrepancies, reduce redundancy, and enhance situational awareness beyond the capabilities of individual data inputs.

21

Publications

852

Citations

94

Authors

32

Institutions

Publications per year

2017–2026

6

Authors

94

  1. GC
    Genshe Chen

    United States Air Force Research Laboratory, United States

    2 publications · 96 citations

    2

    h-index

  2. MS
    Moises Sudit

    University at Buffalo, State University of New York, United States

    2 publications · 56 citations

    2

    h-index

  3. JH
    Jared Holsopple

    Rochester Institute of Technology, United States

    2 publications · 53 citations

    2

    h-index

  4. SJ
    Shanchieh Jay Yang

    Rochester Institute of Technology, United States

    2 publications · 53 citations

    2

    h-index

  5. EB
    Erik Blasch

    United States Air Force Research Laboratory, United States

    1 publications · 72 citations

    1

    h-index

1–5 of 94

Venues

  1. Computers & Security

    3 publications · 83 citations

    3

    h-index

  2. Information Fusion

    1 publications · 38 citations

    1

    h-index

  3. Future Internet

    1 publications · 33 citations

    1

    h-index

  4. Technological Forecasting and Social Change

    1 publications · 12 citations

    1

    h-index

  5. Cluster Computing

    1 publications · 11 citations

    1

    h-index

1–5