Towards the Usefulness of Learning Factories in the Cybersecurity Domain
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle