Smart-AC: A New Framework Concept for Modeling Access Control Policy
Bibliographic record
Abstract
As new technologies grow such as Internet of Things (IoT) and cloud computing, the way how people interact with devices change. The current world of interconnectivity, the heterogeneity of networks, platforms, applications, and the diversity of users make the modernization of security methods inevitable fact. Access Control (AC) is one of these essential security requirements in this domain. The continuous technology propagation forces the need to enhance AC methods, which are presented in the literature by combining features from two or more models based on a given case or scenario. The aim is to enforce AC policy and create secure communication environments. In this paper, we summarize some of the proposed methods with combined features from various AC models in the light of new technologies. Also, we present a deeper look for our idea of finding a methodology for a new dynamic Smart-AC model and a use case, in addition to the challenges to implement it. Our aim is to find a general AC framework to overcome the limitations of the presented AC methods, since they are not generic enough and do not encompass a general concept to tackle the various IT cases. The concept of our Smart-AC method is that it can be oriented to include (or exclude) all (or some) AC features for a given scenario or project, and work as a generic basis to encompass the heterogeneity of all AC models. Our proposed model aims to follow up AC requirements along with technology propagations and developments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".