Behavioral and Demographic Data-Driven Cybersecurity Risk Classification Using K-Means Clustering on Active Internet Users
Bibliographic record
Abstract
This study applied K-Means clustering to categorize cybersecurity risk levels using responses from 173 active internet users collected through a structured questionnaire.The clustering results, evaluated with a Silhouette Score of 0.1361 and Davies-Bouldin Index of 2.71, indicate that K-Means provides the best grouping among the methods tested, but also reveal substantial overlap between high-risk and low-risk individuals.Chi-Square tests showed that age was significantly associated with risk level, while gender and occupation were not, highlighting the limited discriminatory power of broad demographic variables alone.The findings underscore the importance of incorporating detailed behavioral, knowledge-based, and attitudinal data to improve the accuracy and actionable value of risk classification.Methodological innovation in this study lies in the integrated use of clustering validation metrics with statistical tests to empirically assess demographic associations.Limitations include the modest sample size and potential sampling bias, which may affect the generalizability of the results.These outcomes emphasize the need for multidimensional data integration and advanced analytical approaches to enhance cybersecurity risk assessment and guide the development of targeted, evidence-based interventions.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".