Enhancing Cybersecurity Curriculum Development Through European Cybersecurity Framework and Transformer Models

Enhancing Cybersecurity Curriculum Development Through European Cybersecurity Framework and Transformer Models

연구 분야: Safety

학회: International Conference on Availability, Reliability and Security

This work-in-progress paper introduces the Cybersecurity Curriculum Similarity and Coverage Analysis Method (CSCAM), an AI-based tool designed to evaluate the alignment of academic cybersecurity programs with the European Cybersecurity Skills Framework (ECSF). By applying transformer-based language models, CSCAM converts curricular content and ECSF role descriptions into semantic embeddings, enabling precise sentence-level similarity comparisons. The methodology supports a quantitative assessment of curriculum coverage across defined cybersecurity roles. A case study involving multiple European academic programs and job market data demonstrates CSCAM’s ability to identify gaps between educational offerings and industry demands. The analysis reveals varied coverage across ECSF roles. By incorporating job advertisement data, CSCAM generates targeted recommendations to improve curriculum alignment. Additionally, the study underscores the importance of considering remote work trends in curriculum planning. CSCAM provides a scalable, data-driven tool to support curriculum development and ensure responsiveness to the evolving cybersecurity workforce landscape.