Abstract
Modern Connected and Autonomous Vehicles (CAVs) are equipped with an increasing number of Electronic Control Units (ECUs), many of which produce large amounts of data. Data is exchanged between ECUs via an in-vehicle network, with the Controller Area Network (CAN) bus being the de facto standard in contemporary vehicles. Furthermore, CAVs have not only physical interfaces but also increased data connectivity to the Internet via their Telematic Control Units (TCUs), enabling remote access via mobile devices. It is also possible to tap into, and read/write data from/to the CAN bus, as data transmitted on the CAN bus is not encrypted. This naturally generates concerns about automotive cybersecurity. One commonality among most vehicular security attacks reported to date is that they ultimately require write access to the CAN bus. In order to cause targeted and intentional changes in vehicle behavior, malicious CAN injection attacks require knowledge of the CAN message format. However, since this format is proprietary to OEMs and can differ even among different models of a single make of vehicle, one must manually reverse-engineer the CAN message format of each vehicle they target — a time-consuming and tedious process that does not scale. To mitigate this difficulty, we develop LibreCAN, which can translate most CAN messages with minimal effort. Our extensive evaluation on multiple vehicles demonstrates LibreCAN’s efficiency in terms of accuracy, coverage, required manual effort and scalability to any vehicle.
