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

合作研究：数据中毒攻击和基于基础设施的交通状态估计和预测解决方案

• 批准号: 2326341
• 负责人: Yuan Hong
• 金额: $ 16.99万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326341&HistoricalAwards=false
• 关键词: Collaborative; Research; Data; Poisoning; Attacks

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.

该奖项将支持研究运输系统中的“数据中毒”攻击，并开发新的防御方法来增强运输网络安全。借助无处不在的数据和广泛应用的数据驱动方法，数据中毒攻击正成为对交通状态估计和预测（TSEP）的关键网络安全威胁（TSEP），以及与车队管理和交通控制和交通控制有关的决策。这项研究将通过确定新的数据中毒攻击并开发有关基本运输应用的新型防御方法，从而具有深远的社会利益和影响。这项研究还将有助于提高对数据安全的认识，并促进基础设施解决方案的开发，以增强运输安全性。该团队将将研究结果整合到现有课程和新课程中，并建议研究生和本科生，尤其是来自科学和工程研究中人数不足的小组的学生，以参与尖端研究。项目团队成员将通过向K-12学生（尤其是高中生）提供科学和工程学的投入来参加多个外展计划。该团队还将向运输机构，学术界和行业合作伙伴传达研究结果。研究人员将将研究结果转移到实践中，以对现实世界产生重大影响。这项研究将在设计运输数据中毒攻击和开发创新的防御解决方案以确保运输数据安全方面开发新的范式。数据中毒攻击首先是作为对数据扰动（攻击）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衍生的算法，以帮助设计更通用和适用于运输应用程序的攻击模型。该团队还将开发学习模型的复杂目标功能和/或约束的近似方案，并研究深度学习模型上攻击方法的可传递性。为了防止攻击，将通过利用现有和新部署的安全基础架构数据/信息来检测和减轻攻击来开发一个支持基础设施的防御框架。这个新的防御框架将有助于开发一个安全的数据网络，以有效防御对各种应用程序的不同攻击。这项研究还将提供有用的见解，以研究攻击并在其他工程和科学领域开发新颖的防御方法。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。

项目成果

Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks

• DOI: 10.1109/sp54263.2024.00053
• 发表时间: 2023-07
• 期刊: ArXiv
• 作者: Xinyu Zhang;Hanbin Hong;Yuan Hong;Peng Huang;Binghui Wang;Zhongjie Ba;Kui Ren

Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory

• DOI: 10.1145/3639285
• 发表时间: 2023-11
• 期刊: Proceedings of the ACM on Management of Data
• 作者: Xiaochen Li;Weiran Liu;Jian Lou;Yuan Hong;Lei Zhang;Zhan Qin;Kui Ren

DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming

• DOI: 10.48550/arxiv.2312.04738
• 发表时间: 2023-12
• 期刊: ArXiv
• 作者: Shuya Feng;Meisam Mohammady;Han Wang;Xiaochen Li;Zhan Qin;Yuan Hong