Collusion threat profile analysis: Review and analysis of MERIT model
Bibliographic record
Abstract
The MERIT (Management and Education of the Risk of Insider Threat) model was developed based on the CERT/USSS Insider Threat Study (ITS). MERIT model is a system dynamics framework designed to model, understand and assist organizations to mitigate the risk of insider threat [1]. This model's key findings and conclusions relies exclusively on the cases of individual threat agents. However, the reports of the CERT/USSS ITS on which MERIT was based, did examine some cases of collusion, and our examination of these reports shows that these cases presents different personal precursors from those identified in the MERIT model. We further investigated, by examining later ITS done by CERT/USSS and some independent, high profile internal fraud cases (such as WorldCom, Enron, Tyco fraud etc). These further investigations of collusion threat incidents also reveal different personal precursors as compared to individual insider threat incidents. This paper will present the limitations and shortcomings of MERIT model as well as the studies it was based and further argue that MERIT fails to cover a comprehensive pattern analysis (motivational factors and behavioural characteristics) of all forms of insider threat and in particular collusion threat.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".