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Record W4396593599 · doi:10.1109/mitp.2024.3375571

Cybercrime: Understanding the Current State of Literature and Issues Facing CISOs

2024· article· en· W4396593599 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

VenueIT Professional · 2024
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsSimon Fraser UniversityUniversity of Victoria
Fundersnot available
KeywordsCybercrimeCurrent (fluid)State (computer science)Computer scienceComputer securityThe InternetEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

The meteoric rise in cybercrime in recent years has resulted in renewed efforts to stem the potential negative effects of these nefarious activities. In this context, the role of the chief information security officer (CISO) has become one of strategic importance, safeguarding the integrity of the organization’s digital assets. Given the economic impact of cybercrime, it has become critically important to understand the cybercrime-related issues that organizations face. We sought to identify these issues by conducting a bibliographic analysis of cybercrime research. The results identified the most prolific and impactful authors, journals, and countries of publication, the most influential articles, and trends in the literature on cybercrime. The research suggests that interest in the field is wide-reaching with the growth in publications stemming from diverse academic disciplines and geographies. The identified trends represent critical knowledge areas for the CISO that are likely to continue the expansion of the field.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.832
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.046
GPT teacher head0.346
Teacher spread0.300 · 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