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Advancement of Cybersecurity and Information Security Awareness to Facilitate Digital Transformation

2021· book-chapter· en· W3180980481 on OpenAlexaff
Hamed Taherdoost, Mitra Madanchian, Mona Ebrahimi

Bibliographic record

VenueAdvances in information security, privacy, and ethics book series · 2021
Typebook-chapter
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsUniversity Canada West
Fundersnot available
KeywordsComputer securityDigital transformationPaceInformation securityComputer scienceInformation systems securityAppealTransformation (genetics)Internet privacyInformation systemEngineeringWorld Wide WebPolitical scienceManagement information systemsLaw

Abstract

fetched live from OpenAlex

As the pace of changes in the digital world is increasing exponentially, the appeal to shift from traditional platforms to digital ones is increasing as well. Accomplishing digital transformation objectives is impossible without information security considerations. Business leaders should rethink information security challenges associated with digital transformation and consider solutions to seize existing opportunities. When it comes to information security, human beings play a critical role. Raising users' awareness is a meaningful approach to avoid or neutralize the likelihood of unwanted security consequences that may occur during transforming a system digitally. This chapter will discuss cybersecurity and information security awareness and examine how digital transformation will be affected by implementing information security awareness. This chapter will discuss the digital transformation advantages and serious challenges associated with cybersecurity, how to enhance cybersecurity, and the role of information security awareness to mitigate cybersecurity risks.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0050.007
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0220.007

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 head0.025
GPT teacher head0.268
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations6
Published2021
Admission routes1
Has abstractyes

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