A Knowledge Graph-Based Approach to Designing Educational Cybersecurity Games

Knowledge Graphs (KGs) allow structuring complex information in ways that mirror human cognition, enabling the integration of fragmented data into coherent, queryable knowledge bases. In cybersecurity, KGs have been increasingly applied to capture relationships among threats, vulnerabilities, and mitigations, thus supporting advanced threat detection and response. At the same time, their role in education has been recognized, where they enhance interpretability, support personalized learning, and make complex relationships between concepts more transparent. These properties position KGs as a suitable foundation for the design of serious games, where structured, semantically rich knowledge can be translated into engaging, interactive learning experiences. In this study, we present a structured, modular method for applying knowledge graphs to cybersecurity education capable of bridging structured threat intelligence and serious-game design. By constructing a cybersecurity knowledge graph from established standards, extracting its entities and relations, and automatically converting those structures into adaptable attack and defense learning artifacts, our approach can reduce facilitator dependence and enhances realism. A prototype KG and generated card examples demonstrate that the method yields scalable, repeatable, and adaptable learning materials capable of supporting decision-support activities and future empirical validation.