A Comparative Analysis to Advancing the National Cybersecurity Strategy in Saudi Arabia
Bibliographic record
Abstract
Cyberspace has dramatically expanded due to technological advancement. Nowadays, cyberspace is part of daily life experiences and socio-economical activities. Countries all over the world need to have their own National Cybersecurity Strategies (NCSS) to be protected from cyber risks and threats. NCSS states the strength of a given country’s cybersecurity strength concerning the objectives, aims, vision, and cybersecurity mission of a country in question. Previously, many researchers have conducted studies on NCSS by contrasting the National Cybersecurity Strategy between different nations primarily for intercontinental teamwork and coordination of cybersecurity challenges globally. Purposefully, one of the main objectives is to evaluate and assess policy frameworks in various countries to combat the prevailing cyber threats. As a result, from the comparison of many policy frameworks on NCSS of many countries, it was discovered that more effort should put into National Cybersecurity of Saudi Arabia. This paper compares the cybersecurity strategy of Saudi Arabia with the NCSS of other fifteen countries such as the United States of America, Singapore, India, Japan, Malaysia, Kuwait, Canada, UK, China, Egypt, Bahrain, Hong Kong, Russia, Korea, and France. Saudi Arabia rank in cybersecurity has risen to be in the second rank in 2020. Compared to other developed countries, the results found that Saudi Arabia appears to be on the right track in ensuring the safety of its cyberspace.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".