UACML: Unified Access Control Modeling Language
Bibliographic record
Abstract
Incorporating security requirements into system design models is receiving increasing interest. Access control requirements are an important part of overall system security requirements. Existing approaches that incorporate access control requirements into system design models have directly been developed on top of specific access control models. In these approaches, there exists a tight-coupling between the modeling language and underlying access control model(s) on which the modeling language is developed. Consequently, these approaches can only support security requirements for the access control model(s) on which they were developed. We propose an alternative approach in this work by adopting a "metamodel of access control" as a basis for developing a UML-based modeling language. The usage of a metamodel of access control offers at least two benefits: (i) our modeling language is able to represent a variety of access control requirements in a generic way and (ii) our modeling language is independent of specific access control models. By using examples, we demonstrate that our approach is useful for developing a generic modeling language of access control that is simple, yet powerful for representing a variety of access control models.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".