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Record W2942230754 · doi:10.4018/jcit.2019070102

An effective cybersecurity training model to support an organizational awareness program: The Cybersecurity Awareness Training Model (CATRAM). A case study in Canada

2019· article· en· W2942230754 on OpenAlexaboutno aff
Régner Sabillón, Jordi Serra-Ruiz, Víctor Cavaller, J M Jeimy Cano

Bibliographic record

VenueRepositorio Institucional E-DocUR (Universidad Del Rosario) · 2019
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsnot available
Fundersnot available
KeywordsTraining (meteorology)Computer securityComputer scienceKnowledge managementEngineering managementEngineering

Abstract

fetched live from OpenAlex

Traditional cybersecurity, security or information security awareness programs have become ineffective to change people’s behavior in recognizing, failing to block or reporting cyberthreats within their organizational environment. As a result, human errors and actions continue to demonstrate that we are the weakest links in cybersecurity. This article studies the most recent cybersecurity awareness programs and its attributes. Furthermore, the authors compiled recent awareness methodologies, frameworks and approaches. The authors introduce a suggested awareness training model to address existing deficiencies in awareness training. The Cybersecurity Awareness TRAining Model (CATRAM) has been designed to deliver training to different organizational audiences, each of these groups with specific content and separate objectives. The authors concluded their study by addressing the need of future research to target new approaches to keep cybersecurity awareness focused on the everchanging cyberthreat landscape. Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0020.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.279
Teacher spread0.254 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2019
Admission routes1
Has abstractyes

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