Date of Graduation
Statler College of Engineering and Mineral Resources
Lane Department of Computer Science and Electrical Engineering
With the rise of Connected-and-Automated-Vehicle (CAV) technologies on roadways, transportation networks have become increasingly connected through Vehicle-to-Everything (V2X) systems. With access to the additional data from V2X, modern cruise control systems like Adaptive Cruise Control (ACC) are further improved upon to develop systems like Cooperative ACC (CACC) which reduces traffic congestion and increases driver safety and energy efficiency. With that increased connectivity, previously closed vehicle systems are now vulnerable to new security threats which pose new technical challenges. Significant research has been done to strengthen the network against external threats such as denial-of-service attacks (DoS) or passive eavesdropping attacks using network management and cryptographic strategies. Internal threats like data falsification are more challenging to address because they originate from already authenticated sources on the network.
This work suggests a method to locally determine if network data can be trusted utilizing only the intra-vehicle sensors against the network data. It functions by leveraging the synchronization of CACC vehicle stream members to identify potentially malicious data. In the event the network data is determined to be untrustworthy, the vehicle will change its mode of operation to basic ACC where it will disconnect from the vehicle stream and increase the distance from the preceding vehicle. In order to test this approach, an ACC system was created and then modified into a simple CACC system that includes the V2X network data streams. Two common V2X applications were used to show the functionality of both the simple CACC system and the work: a Vehicle-to-Infrastructure (V2I) enabled traffic light and a Vehicle-to-Vehicle (V2V) vehicle stream.
Colon, Alexander Francis, "Mitigating Insider Threats in a Cooperative Adaptive Cruise Control System Using Local Intra-Vehicle Data" (2021). Graduate Theses, Dissertations, and Problem Reports. 10241.