Research on Cybercrime Prevention and Control Strategies Based on K-means++ and PSO Algorithm
Bibliographic record
Abstract
This paper focuses on the field of cybercrime, analysing its global pattern and the effectiveness of policy responses through a multimethod approach. The World Cybercrime Index (WCI) is constructed with the help of experts' experience, which reveals the characteristics of the global cybercrime risk distribution, i.e. Europe and North America have a high prevalence of cybercrime, followed by Asia, and South America and Africa have a low prevalence of cybercrime. The K-means++ clustering algorithm is used to classify the risk of 97 countries/regions, and the results match the actual distribution. In the research of policy effectiveness, we constructed a performance score index and found that there is a mutually reinforcing relationship between cybercrime risk and the level of cybersecurity construction; we defined the security index S, and constructed a regression model by combining the political data and legal density of the ITU, and concluded that the legal measures are the most effective in improving the security index. The particle swarm optimisation algorithm is used to explore the optimal political scenarios, provide decision-making reference for legislators, and analyse the reasons for the differences in legal density, and the research results have important reference value for the formulation of global cybercrime prevention and control policies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".