Beyond the Password: A Data-Driven Analysis of Biometric Security in the Age of AI
Bibliographic record
Abstract
This research presents a comprehensive bibliometric analysis of Biometric research in the cybersecurity domain from 2017 to 2024, utilizing the Scopus database. The study explores 40 publications to discern trends, influential authors, affiliated institutions, countries of origin, and subject areas. Employing bibliometric methods, the analysis includes publication count, co-authorship, keyword co-occurrence, and citation analyses. Results illustrate a rise in publications until 2019, followed by a subsequent decline. Notable authors like Gavrilova, M. and collaborative networks were identified. The research predominantly originates from institutions in the United States, Canada, India, Saudi Arabia, Egypt, and Pakistan. Computer Science emerges as the primary subject area, indicating a predominant interest among researchers. A word cloud depicts prominent terms associated with cybersecurity and biometrics, highlighting key focus areas in the field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.031 | 0.047 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".