Communications of the ACM
Cybersecurity in Self-Driving Cars
New research from the University of Michigan found that, while every new generation of auto tech brings new security risks, the vulnerabilities that come along with advanced mobility are both unprecedented and under-studied.
Credit: Steve Alvey/Michigan Engineering
Researchers at the University of Michigan (U-M), working with Mcity, the nation's largest public-private partnership working to advance connected and automated mobility, have developed the Mcity Threat Identification Model, a tool to help scientists analyze the likelihood of potential cybersecurity threats that must be overcome before autonomous and connected vehicles can be widely adopted.
The new model outlines a framework for considering various factors, including an attacker's skill level and motivation, vulnerable vehicle system components, ways in which an attack could be achieved, and the repercussions.
Andre Weimerskirch, who leads Mcity's cybersecurity working group, says the tool can serve as a blueprint to effectively identify and analyze cybersecurity threats and create effective approaches to making autonomous vehicle systems safe and secure.
The researchers used the model to examine vulnerabilities in automated parking, and found the most likely attacks are a mechanic disabling the range sensors in park-assist or remote parking in order to require additional maintenance, and an expert hacker sending a false signal to a vehicle's receiver to deactivate remote parking.
From University of Michigan News
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