IConPAL: LLM-guided Policy Authoring Assistant for Configuring IoT Defenses

Safety and security concerns surrounding Internet-of-Things (IoT) platforms for smart homes have spurred the development of defense mechanisms to safeguard against unexpected behaviors in accordance with safety and security policies. However, the need to manually craft policies in tool-specific languages increases the burden on humans. Previous attempts to address this issue have fallen short, either lacking portability or requiring human intervention in other forms. Therefore, in this paper, we propose iConPAL, an automated policy authoring assistant for IoT environments. iConPAL accepts a policy description in natural language (English) and translates it into a specific formal policy language. iConPAL leverages the capabilities of modern large language models (LLMs), employs prompt engineering to automatically generate few-shot learning prompts for the LLM, and post-processes the LLM's response to ensure the validity of the translated policy. We implemented a prototype of iConPAL and evaluated it on our curated dataset of 290 policies. We observed that iConPAL successfully translated 93.61% policies, of which 93.57% were semantically correct. iConPAL's high accuracy makes it suitable for assisting ordinary users in drafting policies for smart homes.