Ontology-Aware RAG for Improved Question-Answering in Cybersecurity Education
Project Overview
The document explores the role of generative AI, particularly a retrieval-augmented generation (RAG) approach, in enhancing education within the cybersecurity domain. It introduces the CyberRAG framework, which integrates domain-specific knowledge retrieval with large language models (LLMs) to improve question-answering systems for educational purposes. This innovative approach aims to provide accurate and reliable support to learners, emphasizing the importance of managing uncertainty in the learning process and fostering interactive, inquiry-based experiences in technical fields. Despite the promising outcomes observed in its implementation, the study also identifies significant challenges, including the occurrence of hallucinations in AI responses and limitations in domain knowledge, which must be addressed to fully realize the potential of generative AI in education. Overall, the findings underscore the transformative possibilities of AI technologies while highlighting the need for ongoing refinement and oversight in their application.
Key Applications
CyberRAG
Context: Cybersecurity education aimed at students learning about complex tools and defense techniques.
Implementation: CyberRAG employs a two-step approach: retrieving domain-specific knowledge from a validated knowledge base and validating responses using a knowledge graph ontology.
Outcomes: CyberRAG delivers accurate and reliable responses by integrating LLMs with a cybersecurity knowledge base, effectively managing uncertainty in problem-based learning.
Challenges: Challenges include the risk of hallucinations and the potential for domain knowledge to be inadequate, which could compromise the effectiveness of the AI-generated responses.
Implementation Barriers
Technical
Large language models face challenges such as hallucinations and limited domain-specific knowledge, which reduce their reliability in educational settings.
Proposed Solutions: Implementing ontology-aware validation systems to mitigate hallucinations and improve accuracy of AI-generated content.
Human Resources
Validation by cybersecurity experts for ensuring the accuracy of AI responses is labor-intensive, costly, and time-consuming.
Proposed Solutions: Developing automatic validation approaches using knowledge graph ontologies to ensure accuracy without constant human oversight.
Project Team
Chengshuai Zhao
Researcher
Garima Agrawal
Researcher
Tharindu Kumarage
Researcher
Zhen Tan
Researcher
Yuli Deng
Researcher
Ying-Chih Chen
Researcher
Huan Liu
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Chengshuai Zhao, Garima Agrawal, Tharindu Kumarage, Zhen Tan, Yuli Deng, Ying-Chih Chen, Huan Liu
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai