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The Cybersecurity Awareness Training Model (CATRAM)

2020· book-chapter· en· W4256318103 on OpenAlexaboutno aff

Bibliographic record

VenueAdvances in digital crime, forensics, and cyber terrorism book series · 2020
Typebook-chapter
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsnot available
Fundersnot available
KeywordsSecurity awarenessComputer securityComputer scienceInformation securityIncident responseTraining (meteorology)Knowledge management

Abstract

fetched live from OpenAlex

This chapter presents the outcome of one empirical research study that assess the implementation and validation of the cybersecurity awareness training model (CATRAM), designed as a multiple-case study in a Canadian higher education institution. Information security awareness programs have become unsuccessful to change people's attitudes in recognizing, stopping, or reporting cyberthreats within their corporate environment. Therefore, human errors and actions continue to demonstrate that we as humans are the weakest links in cybersecurity. The chapter studies the most recent cybersecurity awareness programs and its attributes. Furthermore, the authors compiled recent awareness methodologies, frameworks, and approaches. The cybersecurity awareness training model (CATRAM) has been created to deliver training to different corporate audiences, each of these organizational units with peculiar content and detached objectives. They concluded their study by addressing the necessity of future research to target new approaches to keep cybersecurity awareness focused on the everchanging cyberthreat landscape.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.592
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.010
Open science0.0010.001
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 head0.019
GPT teacher head0.239
Teacher spread0.220 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations4
Published2020
Admission routes1
Has abstractyes

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