Ontology-Aware RAG for Improved Question-Answering in Cybersecurity Education
This addresses the need for reliable and safe AI tools in cybersecurity education, though it appears incremental as it builds on existing RAG methods with domain-specific enhancements.
The paper tackled the problem of hallucinations and limited domain-specific knowledge in AI-driven question-answering systems for cybersecurity education by proposing CyberRAG, an ontology-aware retrieval-augmented generation approach, which delivered accurate and reliable responses in experiments on cybersecurity datasets.
Integrating AI into education has the potential to transform the teaching of science and technology courses, particularly in the field of cybersecurity. AI-driven question-answering (QA) systems can actively manage uncertainty in cybersecurity problem-solving, offering interactive, inquiry-based learning experiences. Large language models (LLMs) have gained prominence in AI-driven QA systems, offering advanced language understanding and user engagement. However, they face challenges like hallucinations and limited domain-specific knowledge, which reduce their reliability in educational settings. To address these challenges, we propose CyberRAG, an ontology-aware retrieval-augmented generation (RAG) approach for developing a reliable and safe QA system in cybersecurity education. CyberRAG employs a two-step approach: first, it augments the domain-specific knowledge by retrieving validated cybersecurity documents from a knowledge base to enhance the relevance and accuracy of the response. Second, it mitigates hallucinations and misuse by integrating a knowledge graph ontology to validate the final answer. Experiments on publicly available cybersecurity datasets show that CyberRAG delivers accurate, reliable responses aligned with domain knowledge, demonstrating the potential of AI tools to enhance education.