As cybersecurity threats continue to evolve, the need for advanced tools to analyze and understand complex cyber environments has become increasingly critical. Graph theory offers a powerful framework for modeling relationships within cyber ecosystems, making it highly applicable to cybersecurity. This paper focuses on the development of an enriched version of the widely recognized NSL-KDD dataset, incorporating graph-theoretical concepts to enhance its practical value. The enriched dataset provides a resource for students and professionals to engage in hands-on analysis, enabling them to explore graph-based methodologies for identifying network behavior and vulnerabilities. To validate the effectiveness of this dataset, we employed IBM Auto AI, demonstrating its capability in real-world applications such as classification and threat prediction. By addressing the need for graph-theoretical datasets, this study provides a practical tool for equipping future cybersecurity professionals with the skills necessary to confront complex cyber challenges.