Fundamentals methodology of formation cyber competences at security sector experts and Ukraine defense
Bibliographic record
Abstract
The analysis of the existing systems of training military specialists in cybersecurity issues for the national security and defense spheres in a number of leading countries of the world (USA, UK, Canada, Germany, Poland) was conducted. An analysis of the cybersecurity education system of the US population was also conducted. Data is provided about main educational institutions in these countries where military cybersecurity experts are successfully trained, as well as the training programs offered by the US Department of Homeland Security to train children in the system of pre-school and school education, their parents, teachers and other. The data shows system and programs for the preparation of cybersecurity bachelors in higher educational institutions of the United States. Analyzed the prerequisites, stages of formation and the current state of the training system in Ukraine of cybersecurity specialists. It was determined that the standards for training military specialists in higher education institutions of the security and defense sector of Ukraine did not sufficiently take into account their competence in the cybersecurity basics. The analysis of the basic concepts in the professional-competence approach to the training of cybersecurity specialists has been carried out. The main provisions of the methodology for the development of an integrated system of improving cyber-education of the population and training of specialists in cybersecurity issues for the security and defense sector of Ukraine are proposed. It has been shown that raising the level of population education in cybersecurity issues in Ukraine should begin with pre-school education, as well as introduce a permanent school cyber-education system, which will make it possible to better prepare a child for adulthood in a modern high-tech society. Clarified requirements for cybersecurity education in higher education. In accordance with the best foreign experience, it was suggested that the main efforts in training cybersecurity experts for the security and defense sector should be focused on integrating the scientific, pedagogical and material-technical potentials on a single basis, by forming higher military schools (higher education institutions with specific learning conditions) a new type in the form of an integrated teaching and research and experimental test complex.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".