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

该学院早期职业发展（Career）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、更有效的交通管理策略。尽管有这些好处，但这些组件越来越容易受到网络攻击。为保障网络空间交通基础设施的安全，本项目开展各种场景下的安全分析、防御方案推荐和系统性能评估。结果为公共机构制定标准和政策提供信息，并优先部署有限的国防资源。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)拓展社区大学生的知识领域和技能；3)激励K-12和少数族裔学生从事工程职业；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，特别是从紧急行为和系统级性能的角度。该项目探讨了网络攻击对交通出行和安全的影响，并整合了优化、机器学习和统计推断等不同领域的知识。该项目旨在通过将领域知识（如交通流模型）纳入安全分析，弥合网络安全和交通学科之间的研究差距。这些教育活动为本科生、研究生、社区学院和K-12学生提供未来工程职业所需的技能。该奖项反映了美国国家科学基金会的法定使命，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。

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.