Evaluation of LLM Chatbots for OSINT-based Cyberthreat Awareness

Knowledge sharing about emerging threats is crucial in the rapidly advancing field of cybersecurity and forms the foundation of Cyber Threat Intelligence. In this context, Large Language Models are becoming increasingly significant in the field of cybersecurity, presenting a wide range of opportunities. This study explores the capability of chatbots such as ChatGPT, GPT4all, Dolly,Stanford Alpaca, Alpaca-LoRA, and Falcon to identify cybersecurity-related text within Open Source Intelligence. We assess the capabilities of existing chatbot models for Natural Language Processing tasks. We consider binary classification and Named Entity Recognition as tasks. This study analyzes well-established data collected from Twitter, derived from previous research efforts. Regarding cybersecurity binary classification, Chatbot GPT-4 as a commercial model achieved an acceptable F1-score of 0.94, and the open-source GPT4all model achieved an F1-score of 0.90. However, concerning cybersecurity entity recognition, chatbot models have limitations and are less effective. This study demonstrates the capability of these chatbots only for specific tasks, such as cybersecurity binary classification, while highlighting the need for further refinement in other tasks, such as Named Entity Recognition tasks.