Detecting contradictions from IoT protocol specification documents based on neural generated knowledge graph

Due to the boom of Internet of Things (IoT) in recent years, various IoT devices are connected to the Internet and communicate with each other through network protocols such as the Constrained Application Protocol (CoAP). These protocols are typically defined and described in specification documents, such as Request for Comments (RFC), which are written in natural or semi-formal languages. Since developers largely follow the specification documents when implementing web protocols, they have become the de facto protocol specifications. Therefore, it must be ensured that the descriptions in them are consistent to avoid technological issues, incompatibility, security risks, or even legal concerns. In this work, we propose Neural RFC Knowledge Graph (NRFCKG), a neural network-generated knowledge graph based contradictions detection tool for IoT protocol specification documents. Our approach can automatically parse the specification documents and construct knowledge graphs from them through entity extraction, relation extraction, and rule extraction with large language models. It then conducts an intra-entity and inter-entity contradiction detection over the generated knowledge graph. We implement NRFCKG and apply it to the most extensively used messaging protocols in IoT, including the main RFC (RFC7252) of CoAP, the specification document of MQTT, and the specification document of AMQP. Our evaluation shows that NRFCKG generalizes well to other specification documents and it manages to detect contradictions from these IoT protocol specification documents.