Resiliency in Connected Vehicle Applications: Challenges and Approaches for Security Validation

Abstract

With the proliferation of connectivity and smart computing in vehicles, a new attack surface has emerged that targets subversion of vehicular applications by compromising sensors and communication. A unique feature of these attacks is that they no longer require intrusion into the hardware and software components of the victim vehicle; rather, it is possible to subvert the application by providing wrong or misleading information. We consider the problem of making vehicular systems resilient against these threats. A promising approach is to adapt resiliency solutions based on anomaly detection through Machine Learning. We discuss challenges in making such an approach viable. In particular, we consider the problem of validating such resiliency architectures, the factors that make the problem challenging, and our approaches to address the challenges.