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
21
A framework for threat intelligence extraction and fusion
Yongyan Guo, Zhengyu Liu, Cheng Huang et al. · Computers & Security · 2023 · 37 citations
1–5 of 21
94
2
h-index
University at Buffalo, State University of New York, United States
2 publications · 56 citations
2
h-index
2
h-index
2
h-index
1
h-index
1–5 of 94
32
Charles River Analytics (United States)
Cambridge, United States
2 h-index
5 publications
104 citations

Sichuan University
Chengdu, China
2 h-index
11 publications
391 citations
Intelligent Automation (United States)
Rockville, United States
2 h-index
3 publications
168 citations
University at Buffalo, State University of New York
Buffalo, United States
2 h-index
2 publications
56 citations
Rochester Institute of Technology
Rochester, United States
2 h-index
5 publications
127 citations
IIT@MIT
Cambridge, United States
1 h-index
3 publications
51 citations

Gachon University
Seongnam-si, South Korea
1 h-index
3 publications
99 citations

Technische Universität Darmstadt
Darmstadt, Germany
1 h-index
3 publications
96 citations
1–8 of 32
3 publications · 83 citations
3
h-index
1 publications · 38 citations
1
h-index
1 publications · 33 citations
1
h-index
1 publications · 12 citations
1
h-index
1 publications · 11 citations
1
h-index
1–5