CyberBOT: Towards Reliable Cybersecurity Education via Ontology-Grounded Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2504.00389v1
• Date: Tue, 01 Apr 2025 03:19:22 GMT
• Title: CyberBOT: Towards Reliable Cybersecurity Education via Ontology-Grounded Retrieval Augmented Generation
• Authors: Chengshuai Zhao, Riccardo De Maria, Tharindu Kumarage, Kumar Satvik Chaudhary, Garima Agrawal, Yiwen Li, Jongchan Park, Yuli Deng, Ying-Chih Chen, Huan Liu,
• Abstract summary: In cybersecurity education, accuracy and safety are paramount, and systems must go beyond surface-level relevance to provide information that is both trustworthy and domain-appropriate.<n>We introduce CyberBOT, a question-answering robot that incorporates contextual information from course-specific materials and validate responses using a domain-specific cybersecurity ontology.<n>CyberBOT has been deployed in a large graduate-level course at Arizona State University, where more than one hundred students actively engage with the system through a dedicated web-based platform.
• Score: 13.352385179504482
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advancements in large language models (LLMs) have enabled the development of intelligent educational tools that support inquiry-based learning across technical domains. In cybersecurity education, where accuracy and safety are paramount, systems must go beyond surface-level relevance to provide information that is both trustworthy and domain-appropriate. To address this challenge, we introduce CyberBOT, a question-answering chatbot that leverages a retrieval-augmented generation (RAG) pipeline to incorporate contextual information from course-specific materials and validate responses using a domain-specific cybersecurity ontology. The ontology serves as a structured reasoning layer that constrains and verifies LLM-generated answers, reducing the risk of misleading or unsafe guidance. CyberBOT has been deployed in a large graduate-level course at Arizona State University (ASU), where more than one hundred students actively engage with the system through a dedicated web-based platform. Computational evaluations in lab environments highlight the potential capacity of CyberBOT, and a forthcoming field study will evaluate its pedagogical impact. By integrating structured domain reasoning with modern generative capabilities, CyberBOT illustrates a promising direction for developing reliable and curriculum-aligned AI applications in specialized educational contexts.

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