HEAD Access Control Metamodel: Distinct Design, Advanced Features, and New Opportunities
Bibliographic record
Abstract
Access control (AC) policies are a set of rules administering decisions in systems and they are increasingly used for implementing flexible and adaptive systems to control access in today’s internet services, networks, security systems, and others. The emergence of the current generation of networking environments, with digital transformation, such as the internet of things (IoT), fog computing, cloud computing, etc., with their different applications, bring out new trends, concepts, and challenges to integrate more advanced and intelligent systems in critical and heterogeneous structures. This fact, in addition to the COVID-19 pandemic, has prompted a greater need than ever for AC due to widespread telework and the need to access resources and data related to critical domains such as government, healthcare, industry, and others, and any successful cyber or physical attack can disrupt operations or even decline critical services to society. Moreover, various declarations have announced that the world of AC is changing fast, and the pandemic made AC feel more essential than in the past. To minimize security risks of any unauthorized access to physical and logical systems, before and during the pandemic, several AC approaches are proposed to find a common specification for security policy where AC is implemented in various dynamic and heterogeneous computing environments. Unfortunately, the proposed AC models and metamodels have limited features and are insufficient to meet the current access control requirements. In this context, we have developed a Hierarchical, Extensible, Advanced, and Dynamic (HEAD) AC metamodel with substantial features that is able to encompass the heterogeneity of AC models, overcome the existing limitations of the proposed AC metamodels, and follow the various technology progressions. In this paper, we explain the distinct design of the HEAD metamodel, starting from the metamodel development phase and reaching to the policy enforcement phase. We describe the remaining steps and how they can be employed to develop more advanced features in order to open new opportunities and answer the various challenges of technology progressions and the impact of the pandemic in the domain. As a result, we present a novel approach in five main phases: metamodel development, deriving models, generating policies, policy analysis and assessment, and policy enforcement. This approach can be employed to assist security experts and system administrators to design secure systems that comply with the organizational security policies that are related to access control.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".