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

加强美国基础设施（SAI）是NSF的一项计划，旨在促进以人为本的基础和潜在的变革性研究，以加强美国的基础设施。有效的基础设施为社会经济活力和广泛改善生活质量奠定了坚实的基础。强大、可靠和有效的基础设施刺激私营部门创新，促进经济增长，创造就业机会，提高公共部门服务提供的效率，加强社区建设，促进机会平等，保护自然环境，增强国家安全，并推动美国的领导地位。为了实现这些目标，需要来自科学和工程学科的专业知识。SAI专注于人类推理和决策，治理以及社会和文化过程的知识如何使有效的基础设施的建设和维护能够改善生活和社会，并建立在技术和工程的进步之上。该项目的目标是了解受人们行为影响的美国交通系统的网络风险。虽然先进的安全技术旨在保护网络基础设施，但人们对网络风险的低估及其危险行为可能会增加这些风险。例如，个人车辆通常连接到可从个人账户、车载娱乐系统和通信系统获得的驾驶员的个人数据。如果人们在驾驶或与他人联系时泄露密码等个人信息，攻击者就可以访问这些信息并控制基本的汽车功能。这些风险的影响可能包括拥堵、碰撞、通信中断和互联车辆内的拒绝服务、伤害甚至死亡。该项目测试个人用户、其连接设备以及这些设备所连接的交通基础设施的网络安全漏洞。该项目的一部分是测试个人对选择，风险和异常行为的态度是否可以预测网络风险。一个由不同学者组成的团队应用眼动追踪技术来测试个体差异在多大程度上影响了网络攻击风险的基本比率信息的使用，以告知他们的行为。该项目正在利用这些结果创建一个基于代理的网络模型，以模拟人类的风险行为，并研究其对交通基础设施的影响。该模型在曼哈顿的交通上进行了测试，以模拟校准不良的驾驶员风险评估和被盗凭证的影响，并研究如果攻击者接管了对联网汽车的控制，道路事故和拥堵的后果。该项目利用对个人态度的了解来更好地校准风险，降低攻击的可能性，保护交通基础设施。通过整合行为数据和计算机科学，该项目旨在提高美国国家交通基础设施的安全性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.