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Record W4401980534 · doi:10.1016/j.procs.2024.08.008

HoBACDSL: HoBAC-focused Access Control Domain Specific Language

2024· article· en· W4401980534 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

VenueProcedia Computer Science · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicAccess Control and Trust
Canadian institutionsUniversité du Québec à Rimouski
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceDomain (mathematical analysis)Access controlDomain-specific languageControl (management)Human–computer interactionProgramming languageArtificial intelligenceComputer security

Abstract

fetched live from OpenAlex

Access control (AC) in IoT (Internet of Things) systems presents significant challenges. These systems are evolving, heterogeneous, and dynamic, combined with the lack of appropriate tools to specify and design Access Control Policies (ACP). Despite various proposals, complexity persists, especially in restrictive environments like IoT. In this perspective, Higher-Order Attribute-Based Access Control (HoBAC) was proposed as a general AC model that extends Attribute Based AC (ABAC). It allows the design of flexible AC models and policies applicable to IoT and non-IoT systems. The work presented in this paper focuses on addressing the need for tools to support the adoption of HoBAC for IoT systems by proposing HoBACDSL, a Domain Specific Language (DSL). HoBACDSL abstracts the complexities of HoBAC and makes its concepts more accessible. We illustrate how this DSL is concretely used to specify, and generate AC policies in the Extensible Access Control Markup Language (XACML) standard for a Smart Home use case.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.835
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.000
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 head0.017
GPT teacher head0.314
Teacher spread0.297 · 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