Comparative Analysis of ccTLD Security Policies
Bibliographic record
Abstract
This paper analyzes and compares country code Top Level Domain (ccTLD) administration policies. The study investigated the effect of security-related components of these policies on the ccTLD security to determine whether the strength of the security components had any significant effect on the rate of malicious activities in the domains. To achieve this, thirty ccTLDs were selected based on the Human Development Index (HDI), and the administrative policies of the ccTLDs were analyzed and compared for the content of security-related components. The analysis shows that 40% of the ccTLDs have security policies that can be classified as strong, 47% weak, and 13% have no domain security policies. We verified the hypothesis that the ccTLDs of countries with high HDI tend to have strong domain security-related policies, while ccTLDs of countries with medium and low HDI have weak or non-existent policies. The data analysis also confirmed that the lack of enforceable, strong security-related policies in ccTLD administration results in Internet domains that are vulnerable to abuses. The analysis shows that the number of malicious ccTLD domains (N) is inversely proportional to the rate of attacks (P) for ccTLDs with strong security-related policies, and directly proportional to the rate of attacks for ccTLDs with weak security-related policies. The paper also shows no significant correlation between involvement of governments in the domain registration process and the rate of attacks. The importance of the hybrid governance model that combines bottom-up and top-down security administration of the ccTLDs is emphasized in the paper.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".