Integrating Graph Theoretical Approaches in Cybersecurity Education CSCI-RTED

2025-04-23

AI Summary

The widely used NSL-KDD dataset lacks explicit graph-structured representation, hindering graph-theoretic modeling of network behavior and pedagogical/research applications in vulnerability identification. Method: We propose the first systematically graph-enhanced NSL-KDD dataset, constructed by abstracting network topology into node-edge relational graphs, incorporating semantic features (e.g., traffic dependencies, host interactions), and applying graph embedding techniques for structured feature representation. Classification and threat prediction experiments are conducted using IBM AutoAI under this graph-augmented setting. Contribution/Results: Our dataset fills a critical gap in graph-structured cybersecurity education resources. It significantly improves model interpretability (+23.6%) and classification accuracy (+5.8%), strengthens learners’ relational reasoning capabilities in complex threat scenarios, and demonstrates strong applicability for both pedagogy and industrial deployment.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Enhancing cybersecurity education with graph theory
• Developing enriched NSL-KDD dataset for practical analysis
• Validating dataset effectiveness using IBM Auto AI

Innovation

• Enriched NSL-KDD dataset with graph theory
• Applied IBM Auto AI for validation
• Graph-based methods for threat prediction