Research on Network Crime and Security Strategy Based on K-means Cluster Analysis Model
Bibliographic record
Abstract
Cyber security is one of the important issues in global territorial governance, which concerns the security, stability, economic development and public interests of a country and even the whole world. This paper mainly studies the distribution pattern of global cybercrime and establishes the index system of global cybercrime index (GCI). According to the entropy weight method, the top three countries in the global cybercrime index are Indonesia, Tunisia and Nigeria. Countries with an index size above 3.50 are divided according to different geographical characteristics, and the regions with a high proportion of global cybercrime index are Europe, the Pacific region, the tropical region, the Eastern Hemisphere and the coastal region. The K-means cluster analysis model is established, and it is concluded that the countries with high density of cyber crimes include Indonesia, Tunisia, Nigeria, etc. Countries with high success rates include the United States, Switzerland, Serbia, etc. Countries with high rates of reported cybercrime incidents include Albania, Argentina and Armenia. Countries with high litigation rates include Panama, South Korea and Lithuania. The global distribution of cybercrime presents a relatively common pattern, which requires countries to prevent and improve laws and policies in different regions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".