Automotive hands-on learning platform for ranging sensor attacks 

Published in IEEE International Conference on Consumer Electronics and under review in IEEE Internet of Things Journal

Security is a critical concern in the emergent era of autonomous vehicles. Nevertheless, security challenges in automotive systems are not well understood except by a small set of selected experts. In this paper, we address this problem by developing a novel, flexible exploration platform for automotive security. Our framework, AutoHaL, enables the user to get a hands-on understanding of security compromises. We discuss the unique challenges and requirements in the design of such an exploration platform. We discuss the use of the platform in exploring automotive ranging sensor attacks.