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A Role-Based Multilevel Security Access Control Model

2016· article· en· W2622757767 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.

Bibliographic record

VenueJournal of Computer Information Systems · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAccess Control and Trust
Canadian institutionsToronto Metropolitan UniversityUniversity of Regina
Fundersnot available
KeywordsComputer scienceComputer security modelAccess controlHierarchySecurity serviceComputer securityObject (grammar)Hierarchical database modelLogical securityControl (management)Theoretical computer scienceInformation securitySoftware security assuranceData miningArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a Role-Based Multilevel Security Access Control (RBMSAC) model to address the multilevel security issue. In the proposed model, we introduce the concepts of secure permissions, secure objects (secure sub object hierarchy), and secure operations, and propose the method of decomposing a multilevel-security object into a number of sub objects and organizing them into a tree structure. With the embedded security criterion expressions in the secure sub objects and the embedded security criterion subset in the relevant secure operations, the model achieves the multilevel security access control by evaluating these security criterion expressions using the relevant security criterion subset. In addition to presenting the methods of generating new components, we also analyze the relationships among the new components, and discuss the rationale of the proposed model.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.508

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 studies0.0000.000
Scholarly communication0.0010.005
Open science0.0010.000
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 head0.022
GPT teacher head0.294
Teacher spread0.272 · 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