Towards the Usefulness of Learning Factories in the Cybersecurity Domain
Bibliographic record
Abstract
The success of an organisation depends on its employees’ skills and the extent to which they are developed. Although organisations often assume employees are fit and ready for a new position or new developments in their functions, employees need adequate training before, during and after effective performance in their respective roles. Amongst other important roles, training is significant in problem-solving, continuously improving skills, and creating consistency or culture in the work environment. Nonetheless, the significance of training is often disregarded or not understood by organisations as there are often inadequacies, inconsistencies, and ignorance from the employer. Furthermore, organisations are facing cybersecurity skills shortages. Some specialists leave the profession due to a lack of skills or support. The lack of experienced and qualified cyber security specialists increases the risk of IT system systems being targeted with cyber-attacks. Having insufficient cybersecurity staff, companies may struggle to protect their networks from attacks. Organisations are being placed into a troubling position as the threat landscape continues to evolve. With the growth in volume and sophistication of cyber security attacks, the problem of a skilled workforce is exasperated. In order to support the cybersecurity workforce, this paper proposes the implementation of learning factories. Typically, learning factories have been used in the manufacturing sector. However, the fundamental principles and guiding ideologies can also be applied in the cybersecurity domain. Learning factories provide a mechanism to remove the barriers of entering the field of cybersecurity by cultivating and nurturing a cybersecurity workforce. They enable the broadening of the scope for talent and change our current working practices and tighten the gap between education and experience. The closing of the talent gap is an important imperative for cybersecurity. In this paper, a motivation and description of the functionality of learning factories for cybersecurity is provided. Through this paper the benefits of learning factories will be highlighted in order to show the advantages of active engagements in learning activities, real-world application and information sharing.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".