CAREER: Securing Next-Generation Transportation Infrastructure: A Traffic Engineering Perspective

该学院早期职业发展(CALEAR)项目建立了一个综合研究和教育计划，以调查下一代交通基础设施中的网络安全风险，该基础设施预计将配备先进的传感器、通信系统和边缘计算能力，以提供更安全和更高效的交通管理策略。尽管有这些好处，但这些组件越来越容易受到网络攻击。为了保障网络空间交通基础设施的安全，本项目进行了安全分析，推荐了防御方案，并评估了各种场景下的系统性能。其结果为公共机构制定标准和政策以及优先部署有限的国防资源提供了参考。研究活动与教育和推广目标紧密结合，即1)向本科生和研究生讲授交通系统中的网络安全挑战；2)扩大社区学院学生的知识域和技能集；3)激励K-12和少数族裔学生追求工程职业；4)与交通生态系统中的利益相关者合作。现有的与交通基础设施相关的网络安全文献有限，特别是从突发行为和系统级性能的角度。这个项目探索了网络攻击对交通运输机动性和安全的影响，并结合了来自不同领域的知识，如优化、机器学习和统计推理。该项目旨在通过将领域知识(例如，交通流量模型)纳入安全分析来弥合网络安全和交通学科之间的研究差距。教育活动为本科生、研究生、社区学院和K-12年级的学生培养未来工程生涯的技能。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development (CAREER) project establishes an integrated research and education plan to investigate the cybersecurity risk in next-generation transportation infrastructure, which is envisioned to be equipped with advanced sensors, communication systems, and edge computing capabilities to provide safer and more efficient traffic management strategies. Despite the benefits, these components are increasingly exposed to cyberattacks. To secure transportation infrastructure in cyberspace, this project conducts security analysis, recommends defense solutions, and evaluates system performance under various scenarios. The results inform public agencies for developing standards and policies, and prioritizing deployment of limited defense resources. The research activities are closely integrated with education and outreach objectives, namely, 1) teaching undergraduate and graduate students about cybersecurity challenges in transportation systems; 2) expanding community college students’ knowledge domains and skill sets; 3) motivating K-12 and minority students to pursue engineering careers; and 4) collaborating with stakeholders in the transportation ecosystem.Existing cybersecurity literature related to transportation infrastructure is limited, especially from the perspective of emergent behaviors and system level performance. This project explores the impact of cyber-attacks on transportation mobility and safety and coalesces knowledge from diverse fields such as optimization, machine learning, and statistical inference. The project aims to bridge research gaps between the cybersecurity and transportation disciplines by incorporating domain knowledge (e.g., traffic flow models) into security analysis. The educational activities prepare undergraduate, graduate, community college, and K-12 students with skills for future engineering careers.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.