Deriving Access Control Models based on Generic and Dynamic Metamodel Architecture: Industrial Use Case
Bibliographic record
Abstract
With the rapid propagation of technologies and their heterogenous structure of networks, platforms, applications, devices, etc., controlling access to physical and logical resources becomes a necessary requirement. The existing Access Control (AC) models (also hybrid models) are not sufficient to satisfy the current needs of security requirements with this diversity of technological aspects and computing environments due to fact of being vulnerable to various kinds of attacks and threats. Hence, AC metamodels are proposed to serve as a unifying framework or to work as a general basis to derive multiple AC models as special cases or instances. In the literature, several AC metamodels are presented for various scenarios for centralized (e.g. organizations, industries, …) and decentralized (e.g. cloud computing, internet of things, …) environments to enforce AC policies. Despite some of their advantages, they lack some essential features and have limitations specially with the current technology propagations. In this paper, we propose a dynamic and enhanced architecture for an AC metamodel, and present an industrial use case to explain how this architecture is generic to derive various AC model instances and can be used as a base for any future developments in this domain.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".