EAGER: SAI: Understanding Misperceptions of Cyber Risks to Model and Secure Transportation Infrastructures

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership.

To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.

This project's goal is to understand cyber-risks to the U.S. transportation system that are affected by people's behavior. Although sophisticated security technologies aim to protect cyber infrastructures, people's underestimation of cyber risks, and their risky behavior, can increase these risks. For example, personal vehicles are commonly connected to drivers' personal data available from personal accounts, in-vehicle entertainment systems, and communication systems.

If people reveal personal information such as passwords, while driving or connecting with others, attackers can gain access to that information and control essential automotive functions. The impact of these risks may include congestion, collisions, disruption of communication and denial of service within connected vehicles, injury, or even death. This project tests vulnerabilities in cyber security for individual users, their connected devices, and the transportation infrastructures to which those devices are linked.

One part of this project is testing whether individual attitudes about choice, risk, and deviant behavior predicts cyber-risks. A diverse team of scholars apply eye-tracking technology to test the degree to which individual differences affect the use of base rate information about cyber-attack risks to inform their behavior. The project is using these results to create an agent-based network model to simulate human risk behaviors and study their impact on the transportation infrastructure.

The model is tested on traffic in Manhattan to simulate the impact of poorly-calibrated drivers' risk assessments and stolen credentials, and study the consequences for road accidents and congestion if an attacker takes over the control of a connected automotive vehicle. The project leverages knowledge of individuals' attitudes to better calibrate risk, reduce the odds of attack, protect the transportation infrastructure.

By integrating behavioral data and computer science, the project aims to provide improved security of the U.S. national transportation infrastructure.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.