"Team Develops New 'Attacker' Device to Improve Autonomous Car Safety"
Today's cars and autonomous vehicles use millimeter wave (mmWave) radio frequencies to facilitate self-driving or assisted driving functions that protect passengers and pedestrians. However, this connectivity can also leave them vulnerable to cyberattacks. To improve the safety and security of autonomous vehicles, researchers from the lab of Dinesh Bharadia, an affiliate of the UC San Diego Qualcomm Institute (QI), and faculty member in the university's Jacobs School of Engineering Department of Electrical and Computer Engineering, along with colleagues from Northeastern University developed a novel algorithm designed to simulate an attacking device. The algorithm, which is described in the paper titled "mmSpoof: Resilient Spoofing of Automotive Millimeter-wave Radars using Reflect Array," enables researchers to identify areas where autonomous vehicle security can be improved. The team developed an algorithm that mimics a spoofing attack. Previous attempts to develop an attacking device for testing cars' resistance had limited feasibility, assuming that the attacker can synchronize with the victim's radar signal to initiate an attack, or that both cars are physically connected via a cable. This article continues to discuss the attacker device developed to improve autonomous vehicle security.