The USDOT Intelligent Transportation Systems (ITS) Joint Program Office (JPO) Connected Vehicle (CV) Pilot Deployment Program has led the way in developing operational CV cybersecurity solutions, particularly in systematic misbehavior detection. In preparation for the CV Pilot sites transitioning into an operational state, the Program has created a flexible software/hardware capability to identify device misbehavior and remove these devices from the active CV environment. The resulting open source misbehavior detection approach and underlying code has since been shared with GM, Ford and other private companies for further development and refinement of the original concept.
For the purposes of this early capability, “misbehavior” is defined as any case where a CV device generates a Basic Safety Message (BSM) containing one or more values inconsistent with the corresponding vehicle’s true status, position, or behavior. Criteria used for misbehavior include:
Vehicle manufacturers and private sector CV stakeholders have significant interest in misbehavior detection to ensure cybersecure and safe vehicle operation. A connected vehicle device may exhibit misbehavior from underlying reasons related to being hacked, or simply because of sensor error or malfunction. When misbehavior occurs for any reason, it is time-critical that these devices and vehicles are detected – and safety-critical that other nearby CVs know to ignore BSMs from these vehicles.
The Program is developing two tools to assist misbehavior detection research:
Misbehavior detection capability development and testing requires having known faulty BSM data to compare against known correct BSM data. The Faulty BSM Generator creates a stream of both correct and incorrect BSM data from a record of vehicle movement, or trajectory. The underlying approach uses the Trajectory Conversion Algorithm (TCA) software, an open source tool available on the ITS CodeHub. In this case, the Faulty BSM Generator allows the user to specify the type, frequency, and other attributes of these errors via a control file.
The Misbehavior Detection Evaluator assesses BSMs chronologically by timestamp and location. The tool applies detection algorithms on nearby sequential BSMs and outputs findings to the console. The detection algorithms are designed to be self-contained, taking in BSMs and returning a misbehavior result. The Misbehavior Detection Evaluator uses that result to perform computations and store historical data. The simulator also outputs the overall completion time and the average computational time of each algorithm being tested.
Current misbehavior detection algorithms focus on specific elements of the BSM:
Misbehavior detection testing has been conducted in specific scenarios focused on reported speed and position, for example:
For full access to the Misbehavior Detection tools, or access to the Testing Report, please contact cory.krause@noblis.org.