Preserving and Sharing Knowledge – Extending the UAF Security Views with Libraries, Patterns and Profiles
Bibliographic record
Abstract
Abstract Knowledge and experience are gained during the execution of every project. This knowledge remains in the heads of the engineers, but often is not distributed more widely. In Model‐Based Systems Engineering (MBSE) projects, this knowledge can include problem solving techniques, algorithms, libraries of types, patterns, interfaces, components, etc. One of the ways to preserve this knowledge is by creating libraries of these reusable assets. For example, the newest version of Unified Architecture Framework (UAF) included a library developed by Mitre of 1200 different security controls defined in National Institute of Standards and Technology (NIST) standard 800‐53r5. These controls can be referenced on projects to mitigate many common security risks. Each defined control can be integrated with the corresponding risks, security metrics, mitigating elements, solutions, and so forth. All these elements could then be used to construct Security Patterns showing risks that the security controls can mitigate as well as abstract solutions that can satisfy these controls. Patterns publicly provided as a curated, searchable, solution set library could be leveraged by projects and augmented over time, preserving their Intellectual Property (IP) and knowledge assets. This paper discusses these concepts and methods and demonstrates how they can be applied to improve system security.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.037 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".