Collaborative Research: Data Poisoning Attacks and Infrastructure-Enabled Solutions for Traffic State Estimation and Prediction

该合同将支持研究调查交通系统中的“数据中毒”攻击，并开发新的防御方法来增强交通网络安全。随着无处不在的数据和数据驱动方法在交通运输中的广泛应用，数据中毒攻击正成为交通状态估计和预测（TSEP）以及车队管理和交通控制相关决策的关键网络安全威胁。这项研究将通过识别新的数据中毒攻击和开发新的防御方法，对重要的交通应用产生深远的社会效益和影响。该研究还将有助于提高对数据安全的认识，并促进开发基于基础设施的解决方案，以加强运输安全。该团队将把研究成果整合到现有的和新的课程中，并将建议研究生和本科生，特别是来自科学和工程研究中代表性不足的群体的学生，参与前沿研究。项目组成员将参与多个拓展项目，为K-12学生，特别是高中生提供科学和工程方面的投入。该团队还将向运输机构、学术界和行业合作伙伴传达研究成果。研究人员将把研究成果转化为实践，在现实世界中产生重大影响。该研究将为设计交通数据中毒攻击和开发创新防御解决方案提供新的范例，以确保交通数据安全。数据中毒攻击首先被表述为对数据扰动（攻击）的优化问题的敏感性分析。将开发基于Lipschitz连续性的分析方法和基于半导数的算法，以帮助设计更通用且适用于运输应用的攻击模型。该团队还将开发复杂目标函数和/或学习模型约束的近似方案，并研究攻击方法在深度学习模型上的可转移性。为了防御攻击，将通过利用现有和新部署的安全基础设施数据/信息来开发基础设施支持的防御框架，以检测和减轻攻击。这种新的防御框架将有助于建立一个安全的数据网络，以有效地防御针对各种应用程序的不同攻击。该研究还将为其他工程和科学领域的攻击研究和开发新的防御方法提供有用的见解。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 award will support research to investigate "data poisoning" attacks in transportation systems and develop new defense methods to enhance transportation cybersecurity. With ubiquitous data and widely applied data-driven methods in transportation, data poisoning attacks are becoming a critical cybersecurity threat to traffic state estimation and prediction (TSEP), as well as to decision making related to vehicle fleet management and traffic control. This research will have profound societal benefits and impacts by identifying new data poisoning attacks and developing novel defense methods on essential transportation applications. The research will also help raise awareness of data security and facilitate the development of infrastructure-enabled solutions to strengthen transportation security. The team will integrate research results into existing and new courses and will advise both graduate and undergraduate students, especially students from groups underrepresented in science and engineering research, to participate in cutting-edge research. The project team members will participate in multiple outreach programs by providing inputs in science and engineering from this project to K-12 students, especially high school students. The team will also convey research findings to transportation agencies, the academic community, and industry partners. The researchers will transfer research findings to practice, to make significant impacts in the real world. This research will develop a new paradigm in designing transportation data poisoning attacks and developing innovative defense solutions to ensure transportation data security. Data poisoning attacks are first formulated as sensitivity analysis of optimization problems over data perturbations (attacks). Lipschitz continuity-based analysis methods and semi-derivative based algorithms will be developed to help design attack models that are more general and applicable to transportation applications. The team will also develop approximation schemes of the complex objective functions and/or constraints of learning models and study the transferability of attack methods on deep learning models. To defend against the attacks, an infrastructure-enabled defense framework will be developed by leveraging existing and newly deployed secure infrastructure data/information to detect and mitigate attacks. This new defense framework will help develop a secure data network to effectively defend against different attacks on various applications. The research will also provide useful insights to study attacks and develop novel defense methods in other engineering and science fields.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.