Access Control Metamodels: Review, Critical Analysis, and Research Issues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The new generation of networking environments such as the internet of things (IoT), cloud computing, etc. is emerging and releases new prospects to traditional information systems by merging new technologies and services for seamless access to information sources at anytime and anywhere. Concurrently, this emergence opens new threats to information security and new challenges to controlling access to resources. To ensure security, several techniques have been employed, and access control (AC) is one of the essential security requirements especially for recent networking environments. Various authentication and AC methods are proposed to enforce AC policy and to prevent any unauthorized access to logical/physical assets. The continuous technology upgrades and the diversity of AC models force the need to find AC metamodels with a higher level of abstraction that serves as a unifying framework for specifying any AC policy. AC metamodels are proposed to encompass AC features and are used to derive various instances of AC models and methods. In this paper we review the proposed AC metamodels and their implementation scenarios, we analyze them, their objectives, their limitations, and present current research issues and open questions that still need to be addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it