Insider Threat Risk Prediction based on Bayesian Network
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Rule-based detection methods are effective in identifying known attack patterns or policy violations.
“Rule-based detection is effective in identifying known attack patterns or policy violations, offering control over the detection process for customization to specific security requirements [64], [66].”
From A Review of Recent Advances, Challenges, and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods (2024) · cites this paper as [66]
Cites
Human factors in information security: The insider threat – Who can you trust these days?
Colwill, C. (2009) · Information Security Technical Report · doi:10.1016/j.istr.2010.04.004 · Cited by 319 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Alzaabi, F. R., & Mehmood, A. (2024) · IEEE Access · doi:10.1109/access.2024.3369906 · Cited by 204 · see citing works
“Rule-based detection is effective in identifying known attack patterns or policy violations, offering control over the detection process for customization to specific security requirements [64], [66].”
BackgroundRandive, K., Mohan, R., & Sivakrishna, A. M. (2023) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2023.103434 · Cited by 27 · see citing works
CNN-LSTM-Based Insider Threat Detection Model
Yang, M., Yi, J., Zhu, H., & Tan, L. (2025) · 2025 International Conference on Electrical Automation and Artificial Intelligence (ICEAAI) · doi:10.1109/iceaai64185.2025.10957184 · Cited by 4 · see citing works
Integrating Human Factors into Insider Threat Detection – A Systematic Review
Pathirana, N., Roberts, R., Kalutarage, H., & McDermott, C. D. (2026) · ACM Computing Surveys · doi:10.1145/3798089 · Cited by 1 · see citing works
“The authors in [30] present a Bayesian Network method that integrates technological, organiastional, and human factors for predicting insider threats.”
Influential
- Authors
- Elmrabit, N., Yang, S.-H., Yang, L., & Zhou, H.
- Affiliations
- Glasgow Caledonian University (GB)
- Loughborough University (GB)
- University of Leicester (GB)
- Corresponding author
- Elmrabit, Nebrase
- Venue
- Computers & Security
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 96
- Pages
- 101908
- ISSN
- 0167-4048 (ISSN-L)
- OpenAlex
- W3030742098
- Cited by
- 46
- References
- 1
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 12 · 2023: 5 · 2022: 4 · 2021: 4
- FWCI
- 3.06
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Computers & Security (publishedVersion)
- ResearchOnline (acceptedVersion, cc-by-nc-nd)
- Figshare (submittedVersion, cc-by-nc-nd)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Funders
- National Natural Science Foundation of China
- Horizon 2020
- Topics
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Social Sciences › Sociology and Political Science › Access Control and Trust
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- insider threats, risk prediction, Bayesian networks, malicious insiders, data leakage, dark web, organizational factors, human factors, technical factors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
