Cyber Knowledge Completion Using Large Language Models
This addresses the need for better-informed risk assessments in cybersecurity for CPSs, but it is incremental as it builds on existing LLM and RAG methods.
The paper tackles the problem of incomplete cybersecurity knowledge in Cyber-Physical Systems by proposing a Retrieval-Augmented Generation (RAG)-based approach using Large Language Models to map threat patterns, comparing it to a baseline binary classification model on a small hand-labeled dataset.
The integration of the Internet of Things (IoT) into Cyber-Physical Systems (CPSs) has expanded their cyber-attack surface, introducing new and sophisticated threats with potential to exploit emerging vulnerabilities. Assessing the risks of CPSs is increasingly difficult due to incomplete and outdated cybersecurity knowledge. This highlights the urgent need for better-informed risk assessments and mitigation strategies. While previous efforts have relied on rule-based natural language processing (NLP) tools to map vulnerabilities, weaknesses, and attack patterns, recent advancements in Large Language Models (LLMs) present a unique opportunity to enhance cyber-attack knowledge completion through improved reasoning, inference, and summarization capabilities. We apply embedding models to encapsulate information on attack patterns and adversarial techniques, generating mappings between them using vector embeddings. Additionally, we propose a Retrieval-Augmented Generation (RAG)-based approach that leverages pre-trained models to create structured mappings between different taxonomies of threat patterns. Further, we use a small hand-labeled dataset to compare the proposed RAG-based approach to a baseline standard binary classification model. Thus, the proposed approach provides a comprehensive framework to address the challenge of cyber-attack knowledge graph completion.