Prosper

We explore the application of Large Language Models (LLMs) (specifically GPT-3.5-turbo) to extract specifications and automating understanding of networking protocols from Internet Request for Comments (RFC) documents. LLMs have proven successful in specialized domains like medical and legal text understanding, and this work investigates their potential in automatically comprehending RFCs. We develop Artifact Miner, a tool to extract diagram artifacts from RFCs. We then couple extracted artifacts with natural language text to extract protocol automata using GPT-turbo 3.5 (chatGPT) and present our zero-shot and few-shot extraction results. We call this framework for FSM extraction 'PROSPER: Protocol Specification Miner'. We compare PROSPER with existing state-of-the-art techniques for protocol FSM state and transition extraction. Our experiments indicate that employing artifacts along with text for extraction can lead to lower false positives and better accuracy for both extracted states and transitions. Finally, we discuss efficient prompt engineering techniques, the errors we encountered, and pitfalls of using LLMs for knowledge extraction from specialized domains such as RFC documents.