Cybersecurity Training in Norwegian Critical Infrastructure Companies
Bibliographic record
Abstract
Human preparedness is a critical aspect of critical infrastructure (CI) cybersecurity. Many efforts, including educational curricula and training programs, have been taken at both national and company level to ensure human preparedness in CI incident response. These efforts are usually based on corporate requirements or external guidelines and policies. However, the best practices recommended for these efforts in the literature differ significantly from the measures implemented in CI companies. For this reason, we compared state of practice in cybersecurity awareness and training in selected CI companies with the recommendations in literature, aiming to identify the areas that CI companies need to increase efforts for further security implementations. Specifically, we conducted interviews (n=7) and sent out questionnaires to cybersecurity personnel (n=11) in different CI sectors of Norway. The collected data were analyzed to establish the commonalities, differences, and areas of concern among the interviewees, with respect to certain critical attributes. All Norwegian companies involved in the study offered some type of awareness or training activities to their employees, but these activities varied greatly in the level of maturity. Besides, we noted several limitations in methods and contents. According to many participants, the team skills, communication skills, and managerial skills were often inadequately developed. Additional limitations in delivery methods were noticed, too. Finally, we suggested the solutions from the best practices in the literature, and pointed out the areas where the literature has not provided effective measures.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".