Modern autonomous vehicles are not built with security in mind. The increased sensing, computation, control capabilities, and task complexity have introduced security concerns beyond traditional cyber-attacks. By injecting malformed data, by spoofing sensors, by tampering with controllers, and even by manipulating the environment, an attacker can compromise the integrity and even take control over the functionality of such cyber-physical systems. Examples of attacks have recently been demonstrated on a variety of systems which include the rerouting of drones, the hijacking of vessels by Global Positioning System (GPS) spoofing, and the use of wireless connectivity to take over the steering and brakes of automobiles. Several solutions have been pursued in recent years to solve this problem, yet the bulk of cyber-physical system security literature is focused on the detection and estimation of malicious attacks without considering the context, risk or consequences, much less the intent of the attack. Predicting the intention, by contrast, may yield more information about the attack and thus offer defense mechanisms. This research focuses on the development of techniques to identify, predict, and mitigate malicious intentions of autonomous vehicles, seeking to develop fundamental methods for estimating risk and consequences of malicious attacks, identifying malicious intent, and defending, controlling, and reconfiguring the compromised system.
This project will provide fundamental approaches to increase resiliency in autonomous vehicles. Specifically, the proposed research includes: 1) new techniques to estimate risk and consequences of attacks, leveraging knowledge about the system model and reachability-based analysis; 2) machine learning-based and control-level intent inference methods; and 3) the development of policies for resilient planning and control to ensure continuous operation of the system with closed-loop performance guarantees. To better develop and assess the security techniques proposed in this work, realistic case studies will be implemented using state-of-the-art unmanned aerial and ground vehicles with different sensing, computation, and communication capabilities to facilitate their transition into practice. The proposed research is also applicable to cyber-physical systems broadly and will contribute directly to the development of safe autonomous systems. Additionally, as part of this project, a major emphasis will be given to education and outreach including the development of novel curriculum activities centered on the topic of resiliency in robotics, involvement of undergraduate and graduate students in research, and collaborations with industry to train the next generation workforce on cyber-physical system security problems and mitigation schemes.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
