Enhancing Cybersecurity Curriculum Development: AI-Driven Mapping and Optimization Techniques
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Date
2024-07-30
Authors
Dzurenda, Petr
Ricci, Sara
Sikora, Marek
Stejskal, Michal
Lendak, Imre
Adao, Pedro
0000-0002-4366-3950Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Association for Computing Machinery
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Abstract
Cybersecurity has become important, especially during the last decade. The significant growth of information technologies, internet of things, and digitalization in general, increased the interest in cybersecurity professionals significantly. While the demand for cybersecurity professionals is high, there is a significant shortage of these professionals due to the very diverse landscape of knowledge and the complex curriculum accreditation process. In this article, we introduce a novel AI-driven mapping and optimization solution enabling cybersecurity curriculum development. Our solution leverages machine learning and integer linear programming optimization, offering an automated, intuitive, and user-friendly approach. It is designed to align with the European Cybersecurity Skills Framework (ECSF) released by the European Union Agency for Cybersecurity (ENISA) in 2022. Notably, our innovative mapping methodology enables the seamless adaptation of ECSF to existing curricula and addresses evolving industry needs and trend. We conduct a case study using the university curriculum from Brno University of Technology in the Czech Republic to showcase the efficacy of our approach. The results demonstrate the extent of curriculum coverage according to ECSF profiles and the optimization progress achieved through our methodology.
Description
Citation
ARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security. 2024, p. 1-10.
https://dl.acm.org/doi/10.1145/3664476.3670467
https://dl.acm.org/doi/10.1145/3664476.3670467
Document type
Peer-reviewed
Document version
Published version
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Language of document
en