Cyber security trend analysis using web of science: A bibliometric analysis
Bibliographic record
Abstract
Post COVID-19, it is almost certain that most IT/ ITES enabled industries will harness the capabilities of the cloud to promote remote working culture With organizations going online, it is evident that probability of cyber-attacks will spike exponentially Therefore it is very crucial for all digitally enabled industries to keep abreast with the current trends, concerns, and on-going research in the field of cyber-security (Gochhait, Shou, & Fazalbhoy, 2020) To enhance this study, the research has been conducted from the year when the first paper in the field of cyber security was published to date,which is from 1998-2020 The extensive trend analysis has been conducted using bibliometric analysis taking into consideration various parameters for analysis The research uses Web of Science directory for data analysis, studying approximately 2184 records to enlighten the scholars around the world This research will help academicians, students, and experts to get a complete idea of the development of cyber-security as a research field The analysishas revealed apositive growth in the literature The sudden growth of publications was found after 2010 with about 300 published yearly in recent past To name a few, IEEE, Elsevier and Springer were amongst the most popular publications for quality papers on cyber security Countries like the US, UK, Netherlands, Switzerland, Germany, and Canada have contributed significantly to the research related to cyber security © 2020 Ubiquity Press All rights reserved
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.135 | 0.164 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".