Automatic Generation of Cybersecurity Teaching Cases Using Large Language Models

Abstract

Higher education in cybersecurity faces significant challenges in developing practical and innovative offensive-defensive teaching cases. We present an automated framework for generating cybersecurity teaching cases using Large Language Models (LLMs), designed specifically for university-level cybersecurity education. The framework leverages the deep learning capabilities of LLMs and Artificial Intelligence Generated Content (AIGC) technology to enable intelligent construction and assessment of teaching cases. Our system allows instructors to automatically generate multidimensional teaching cases encompassing both known and potentially unknown security threats, based on parameters including network architecture, service configuration, security requirements, and network topology. Through prompt engineering techniques, the system enables fine-tuning of generated cases to accommodate diverse educational objectives and student proficiency levels. The framework incorporates an assessment module employing semantic analysis to provide automated multidimensional evaluation of student solutions, establishing a comprehensive pedagogical cycle. Empirical studies demonstrate that this framework significantly enhances the efficiency and quality of practical cybersecurity education, provides a replicable paradigm for vertical AI applications in higher education, and offers a novel approach to addressing resource constraints in university-level cybersecurity talent development.