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Bridging the Gap: A Pragmatic Approach to Generating Insider Threat Data
Open accessAbstract
The threat of malicious insider activity continues to be of paramount concern in both the public and private sectors. Though there is great interest in advancing the state of the art in predicting and stopping these threats, the difficulty of obtaining suitable data for research, development, and testing remains a significant hinderance. We outline the use of synthetic data to enable progress in one research program, while discussing the benefits and limitations of synthetic insider threat data, the meaning of realism in this context, as well as future research directions.
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“The CMU CERT dataset (Glasser and Lindauer 2013) is a synthetic insider threat dataset developed by the CERT division of Carnegie Mellon University.”
From BRITD: behavior rhythm insider threat detection with time awareness and user adaptation (2024) · cites this paper as Glasser and Lindauer 2013
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In cybersecurity datasets such as CERT [30] and UNSW-NB15 [31], such anomalies often correspond to insider accounts, compromised devices, or misconfigured hosts that diverge from expected interaction patterns.”
From A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection (2025) · cites this paper as [30]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Cybersecurity graphs often contain millions of nodes and edges that evolve in near real time, yet many existing HGNN architectures remain too computationally expensive for production use [30], [43].”
From A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection (2025) · cites this paper as [30]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Insider threat events were injected into the normal behavior logs.”
From BRITD: behavior rhythm insider threat detection with time awareness and user adaptation (2024) · cites this paper as Glasser and Lindauer 2013
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This dataset simulates a series of behavior logs (including login logs, HTTP logs, file logs, email logs, device logs, and so on) of 4,000 employees over 516 days.”
From BRITD: behavior rhythm insider threat detection with time awareness and user adaptation (2024) · cites this paper as Glasser and Lindauer 2013
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“the CERT dataset we used in the experiments is a synthetic dataset.”
From BRITD: behavior rhythm insider threat detection with time awareness and user adaptation (2024) · cites this paper as Glasser and Lindauer 2013
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In the citing paper
“Cybersecurity graphs often contain millions of nodes and edges that evolve in near real time, yet many existing HGNN architectures remain too computationally expensive for production use [30], [43].”
From A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection (2025) · cites this paper as [30]
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Metadata
- Authors
- Glasser, J., & Lindauer, B.
- Affiliations
- Software Engineering Institute (US)
- Venue
- 2013 IEEE Security and Privacy Workshops
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 98-104
- OpenAlex
- W1991210879
- Cited by
- 344
- References
- 17
- Citations by year
- 2026: 39 · 2025: 43 · 2024: 45 · 2023: 37 · 2022: 31 · 2021: 32 · 2020: 34 · 2019: 21 · 2018: 26 · 2017: 12 · 2016: 5 · 2015: 7 · 2014: 9 · 2013: 3
- FWCI
- 6.43
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Security and Privacy Workshops (SPW) (publishedVersion)
- Funders
- U.S. Department of Defense (FA8721-05-C-0003, Contract No. FA8721-05-C-0003)
- Carnegie Mellon University
- Defense Advanced Research Projects Agency
- Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- insider threats, synthetic data, insider threat detection, data scarcity
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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.
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