UC Santa Cruz

Unmanned Vehicles (UVs), including aerial, sea, and ground vehicles, include remotely operated vehicles and autonomous vehicles. The use of these vehicles is increasing rapidly, from military operations to the consumer space. UVs assess their environment (or relay it to the remote operator) with a variety of sensors and actuators that allow them to perform specific tasks such as navigating a route, hovering, or avoiding collisions. So far, UVs tend to trust the information provided by their sensors to make navigation decisions without data validation or verification. Therefore, attackers can exploit these limitations by feeding erroneous sensor data to disrupt or take control of the system. In this paper, we leverage the Dynamic Data Driven Application Systems (DDDAS) paradigm to design and implement an architecture for securing unmanned vehicles. We argue that DDDAS principles are a perfect fit to secure feedback-control systems with protections that classical security mechanisms cannot provide. In particular, by using exact models of the vehicle dynamics, we can compare and correlate their expected behavior (given by the models) with the values from data acquisition. If there is a persistent anomaly, we can replace sensor values with models, or fuse other sensors to replace the missing ones, enabling the vehicle to maintain safety in the immediate future.