Imagine your self-driving car taking you to the wrong location, demanding a Bitcoin ransom, or refusing to start due to a perceived cyberattack. These scenarios, highlighted by University of Michigan researchers at Mcity, underscore the critical cybersecurity challenges facing the widespread adoption of autonomous vehicles. As automotive technology advances, so do the vulnerabilities, creating unprecedented risks that demand thorough investigation and robust solutions.
To address these growing concerns, Mcity, a leading public-private partnership for advancing connected and automated mobility, has developed a crucial tool: the Mcity Threat Identification Model. This innovative framework serves as an essential self driving car tool for academic and industry researchers. It enables a systematic analysis of potential cybersecurity threats, evaluating both their likelihood and potential severity. The model meticulously considers various factors, including attacker skills and motivations, vulnerable vehicle components, attack methods, and potential repercussions spanning privacy breaches, safety hazards, and financial losses. This tool is pioneering in its focus, specifically designed to tackle the unique cybersecurity landscape of automated vehicles.
Mcity Threat Identification Model for Self-Driving Car Cybersecurity
Understanding the Landscape of Cybersecurity Threats for Self Driving Cars
“Cybersecurity is an overlooked area of research in the development of autonomous vehicles,” states Andre Weimerskirch, the white paper’s lead author, head of Mcity’s cybersecurity working group, and VP of cybersecurity at Lear Corp. “Our self driving car tool is a vital initial step in tackling these challenges. It provides a clear blueprint for identifying and analyzing cybersecurity threats effectively, paving the way for robust security measures in autonomous vehicle systems.”
Self-driving cars, categorized as cyber-physical systems, bridge the physical and digital realms, making their security paramount. These vehicles are susceptible to both conventional cyber threats and novel vulnerabilities unique to their autonomous nature. Familiar threats include data breaches targeting personal and financial data, spoofing attacks that manipulate vehicle information systems, and denial-of-service attacks capable of disabling vehicle functions.
However, self driving cars also introduce entirely new categories of threats. These include malicious actors gaining control of a vehicle, potentially holding it or its passengers for ransom. Furthermore, sophisticated thieves could exploit autonomous features to redirect vehicles to illicit locations.
The interconnected nature of self driving cars extends the threat surface to broader networks. Financial systems processing payments, traffic infrastructure (sensors, signals), power grids, and even personal home networks become potential entry points for cyberattacks. The convenience of connected features, such as a car automatically adjusting home settings upon arrival, paradoxically creates vulnerabilities. A compromised vehicle system could grant hackers unauthorized access to connected home networks, leading to burglaries and other security breaches.
Mcity Threat Identification Model: A Key Self Driving Car Tool
The Mcity Threat Identification Model stands out as a vital self driving car tool, offering a structured approach to risk assessment. Researchers applied this model to analyze vulnerabilities within automated parking systems, including both parking assist and advanced remote self-parking functionalities. Their analysis revealed that common threats include mechanics intentionally disabling sensors to inflate maintenance needs, and expert hackers manipulating remote parking systems. These scenarios were rated a ‘6’ on a 10-point probability scale.
The most impactful threat identified was a skilled thief exploiting remote parking signals to steal a vehicle, rated a ‘7’ on the impact scale. This highlights the critical need for robust security measures to protect against even seemingly minor vulnerabilities in self driving car systems.
“Without comprehensive and reliable cybersecurity for autonomous vehicles, their systems, and supporting infrastructure, the widespread adoption of these vehicles is simply not achievable,” emphasizes Huei Peng, Mcity director and the Roger L. McCarthy Professor of Mechanical Engineering. “Investing in research to develop Self Driving Car Tools like the Threat Identification Model is essential to overcome the obstacles hindering the broad deployment of connected and automated vehicle technology.”
The detailed findings and methodology of the threat model are available in the white paper, “Assessing Risk: Identifying and Analyzing Cybersecurity Threats to Automated Vehicles,” and the research paper “Risk Assessment for Cooperative Automated Driving.” These resources further solidify the Mcity Threat Identification Model as a foundational self driving car tool for navigating the complex cybersecurity landscape of autonomous vehicles.
