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Record W7077279172 · doi:10.1080/17517575.2025.2529282

A cybersecurity framework for enhancing Small and medium-sized enterprises (SMEs) security posture using user behaviour analytics

2025· article· en· W7077279172 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

VenueEnterprise Information Systems · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsBlueprintNISTWork (physics)AnalyticsFace (sociological concept)Path (computing)Big data

Abstract

fetched live from OpenAlex

Small and Medium-sized Enterprises (SMEs) face sophisticated cyber threats yet lack advanced, affordable defenses. This study outlines a multi-layered cybersecurity blueprint that operationalises user-behaviour analytics, integrates with the TRIVI business-intelligence model, and maps to all five NIST Cybersecurity Framework functions. It recommends a cloud-friendly implementation path - illustrated with Elastic Stack - that converts existing customer data into enriched security intelligence, enabling proactive, standards-driven threat detection and compliance. The design offers four benefits: SME-specific tailoring, seamless TRIVI integration, NIST-aligned guidance, and cost-effective scalability. The blueprint aims to elevate SME cyber-resilience; future work will validate and refine the approach.

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)0.001
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 score0.896
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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