The Impact of the US on the Development of International Cybersecurity Law: Legal Challenges and Emerging Norms
Bibliographic record
Abstract
The rise in cyber threats and assaults in the current digital era has made cyber security an essential field that poses major risks to individuals, organizations, and nations. Numerous national and international cyber security laws and regulations have been developed in response to these evolving challenges. The efficiency of the country's present cyber security laws and policies is evaluated in this article in light of the growing sophistication and frequency of cyber-attacks. The National Institute of Standards and Technology (NIST) Cyber security Framework and important laws like HIPAA, GLBA, FISMA, CISA, CCPA, and the DOD Cyber security Maturity Model Certification are highlighted in this comprehensive framework that was developed by the US government. The report examines how these restrictions affect various industries and looks at patterns in data on cybercrime from 2000 to 2022. The results emphasize the difficulties, achievements, and necessity of ongoing adaptation in the face of changing cyber threats.
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 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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".