Self-Driving and Connected Cars - Fooling Sensors and Tracking Drivers

Explore the security vulnerabilities and privacy concerns surrounding automated and connected vehicles in this Black Hat conference talk. Delve into two critical aspects of modern transportation technology: the security of autonomous vehicles and the privacy implications of connected vehicles. Discover how sensors in automated vehicles, including LiDAR, radar, and cameras, can be compromised through remote attacks using common hardware, potentially disrupting automation systems. Learn about effective blinding, jamming, replay, relay, and spoofing attacks demonstrated in laboratory experiments, along with proposed software and hardware countermeasures to enhance sensor resilience. Examine the feasibility of location tracking attacks on connected vehicles, even with limited network coverage, through an empirical study conducted at the University of Twente. Gain insights into graph-based approaches for determining optimal eavesdropping locations and understand the financial resources required for vehicle tracking. Evaluate the effectiveness of pseudonym change strategies and a proposed privacy metric for quantifying vehicle privacy levels in the presence of mid-sized attackers. Recognize the urgent need to address these security and privacy challenges to prevent cost-efficient, scalable passive surveillance operations in the evolving landscape of automotive technology.

Self-Driving and Connected Cars: Fooling Sensors and Tracking Drivers