ABSTRACT

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.

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