LLM ‐Driven Sustainable Cybersecurity for Future Intelligent Transportation System: RAG ‐Based Rule Generation and Edge‐Optimisation for Roadside Infrastructure

With the rapid development of connected autonomous vehicles, intelligent traffic systems are increasingly integrating Roadside Unit (RSU). However, some edge devices in the RSU telecommunication network with limited computing power, due to their simple structures, struggle to implement complex security measures, making them vulnerable nodes in RSU networks and prime targets for cyberattacks. Hence, technologies such as intrusion detection are essential for providing security. However, designing an intrusion detection method that is both efficient and lightweight enough for deployment on RSU devices remains an urgent challenge. To settle these issues, this paper proposes a novel intrusion detection rule generation method that optimise LLM‐Generated Intrusion Detection Rules with support vector machine (SVM). This approach automates the generation of intrusion detection rules for new attack types and employs SVM to optimise the parameter values within these rules, making the LLM‐generated rules more accurate. The generated detection rules are designed to achieve effective intrusion detection on most edge devices, providing a robust solution for enhancing the security of the RSU network. After the verification of the real machine experiment, the proposed IDS has a detection accuracy of 98.89% for the processed attack types.