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Record W4229050620 · doi:10.3390/jcp2010004

HEAD Access Control Metamodel: Distinct Design, Advanced Features, and New Opportunities

2022· article· en· W4229050620 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Cybersecurity and Privacy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicAccess Control and Trust
Canadian institutionsCegep de Sept IlesUniversité du Québec à Rimouski
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsComputer scienceAccess controlCloud computingMetamodelingComputer securityContext (archaeology)Software engineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.107
GPT teacher head0.355
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it