NeoCyberKG: Enhancing Cybersecurity Laboratories with a Machine Learning-enabled Knowledge Graph

The hands-on lab is a critical component of cybersecurity education. There lacks of a coherent way to manage existing labs to provide a practical learning plan for learners in the cybersecurity area. Previous studies utilized the word embedding technologies to construct a knowledge graph and adopt it as a learning guide for students, but this approach has its limitations. In this paper, we present a new approach based on latent semantic analysis (LSA) method to replace word embedding in previous studies as it is more appropriate in a small-size corpus, and it is also able to create a mapping that connects both the topic of each lab and concepts contained in each lab. We use LSA to identify relevant semantic relations, extract relevant lab problems, and construct knowledge graphs from lab contents related to cybersecurity topics. We utilize the output of this study by establishing a web-based lab environment for students that: 1. providing lab index and searching, which contains concepts and knowledge extract from each lab. 2.building a recommendation/guidance system for cybersecurity labs and suggesting more relevant labs based on users learning preferences and past lab history to maximize learning outcomes. To measure the effectiveness of the proposed solution, we conducted a use case study and collected survey data from a graduate-level cybersecurity class at a public university. Our study shows that users tend to gain enhanced learning outcomes and express more interest in the cybersecurity area by leveraging the knowledge graph as a learning guide.