Ranking Threats to Determine the Cost of Protecting Information in a Cybersecurity Environment
Bibliographic record
Abstract
The main purpose of the study is to rank certain threats in order to establish further costs for ensuring the protecting of information in the cybersecurity system.Research methodology is a set of methods that form a methodological approach.The main ones are the ranking method through the theory of fuzzy relations and the expert-step method.As a result, due to the ranking of the threats of the selected object, the permissible intensity of the decrease in the level of security and the costs of its provision were determined using the proposed methodology.The results obtained implied the use of modern ranking methods according to the given parameters.In our case, the results obtained allowed us to rank the existing list of threats in the cybersecurity system.The benefit of such results lies in the formation of an information basis for the adoption and implementation of management decisions.The study is limited by selecting only one socio-economic system and its information.The results obtained out in the article have practical and scientific value through a methodical approach to form requirements for the security of the cybersecurity system itself and information of a single object.In the future, more complex socio-economic systems and their cybersecurity should be chosen to apply the methodological approach proposed in the article.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".