Vehicle-RNA: Reverse Engineering With Dynamic Time Warping for Automotive Protocol

As modern vehicles evolve and offer advanced functions managed by numerous ECUs, the potential for security threats increases due to the presence of expanded threat surfaces. Although research into Intrusion Detection Systems (IDS) and automotive fuzzing has progressed, such systems often rely on publicly available CAN data because proprietary CAN DBCs limit access to essential information. This hampers the ability to counter sophisticated attacks, highlighting the need for precise mapping of CAN IDs and payloads. CAN reverse engineering is crucial but is challenged by time-consuming manual analysis and incomplete PID information. To overcome these limitations, we propose theVehicle-RNA framework, which employs machine learning and dynamic time warping to automate the identification of CAN ID functions. TheVehicle-RNA framework is particularly effective in scenarios with limited data, achieving an F1-score of 0.9388 and accurately identifying 10 CAN IDs previously unanalyzed byOpenDBC, which represents a significant advancement in automotive cybersecurity.