CAREER: Characterizing Attack Resilience of Multi-agent Dynamical Systems with Applications to Connected Autonomous Vehicles

职业：表征多智能体动态系统的攻击弹性及其在联网自动驾驶汽车中的应用

• 批准号: 2236537
• 负责人: Shaunak Bopardikar
• 金额: $ 50万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-15 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236537&HistoricalAwards=false
• 关键词: CAREER; Characterizing; Attack; Resilience; Multi

Our society is currently witnessing a surge in new transportation technologies that include autonomous vehicles such as self-driving cars, automated buses, and drones. These technologies promise several benefits such as reduced roadway fatalities and congestion, and efficient last-mile delivery. However, the race to achieve autonomy can make autonomous vehicles increasingly susceptible to attacks on their sensors, communications, and control signals. This CAREER project will create an integrated research and education program focusing on the pressing need to make mobile dynamical systems (or agents) including autonomous vehicles resilient – guaranteeing that they meet their intended objectives, even under sensing, communication, and control attacks. There are three core research objectives. The first involves designing a novel modeling and analysis framework based on game theory that factors in the inherent asymmetry when a single agent operates in adversarial environments. The second is to extend the framework to include cooperation and the use of learning between multiple agents to collectively minimize the impact of attacks. The third is to address the scalability challenge that arises in solving the resulting games with limited onboard computation. The proposed methodology will be evaluated through realistic emulations using a ground robotic testbed and scenarios with full-sized autonomous vehicles. The educational plan includes creating a pool of activities based on innovative games related to attack resilience that will combine engineering and the arts for effective student engagement and personnel training. Presentations to the public and law enforcement agencies will help shape future practices for root-cause analyses of security incidents and deter attacks.This project will result in a holistic framework to study the mathematical underpinnings of security problems arising in multi-agent dynamical systems. This framework will be a major advancement compared to existing frameworks that focus on specific aspects of the security problem. The project will advance multiple sub-fields in game theory and computation. First, it will combine multi-agent security games and learning techniques to provide a novel solution to attacks that leverage environmental uncertainty. Second, this work will build on the progress in randomized algorithms to solve large optimization problems arising in multi-agent games. The approach will enable a new probabilistic paradigm for robust multi-criterion optimization and multi-agent learning. It will characterize the tradeoff between the system’s attack resilience and the computation involved. Third, layered evaluations that include a real mobility testbed with autonomous vehicles will facilitate the transition of this framework to real-world applications.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.

我们的社会目前正在目睹新的运输技术激增，其中包括自动驾驶汽车，例如自动驾驶汽车，自动驾驶汽车和无人机。这些技术承诺有几种好处，例如减少道路死亡和拥塞以及有效的最后一英里。但是，实现自动驾驶汽车的竞赛可以使自动驾驶汽车越来越容易受到传感器，通信和控制信号的攻击。该职业项目将创建一个集成的研究和教育计划，重点是使移动动态系统（或代理）在内的紧迫需求，包括自动驾驶汽车的弹性 - 即使在敏感性，沟通和控制攻击下，它们也能达到其预期的目标。有三个核心研究目标。第一个涉及基于游戏理论设计一种新颖的建模和分析框架，该框架是在对抗环境中单个代理运行时固有不对称性的因素。第二个是将框架扩展到包括合作和在多个代理之间学习的使用，以共同最大程度地减少攻击的影响。第三个是解决在解决产生的游戏中遇到的可伸缩性挑战，并有限地计算。提出的方法将通过使用全尺寸自动驾驶汽车的地面机器人测试床和场景来评估。教育计划包括基于与攻击弹性有关的创新游戏创建一系列活动，该活动将结合工程学和艺术，以进行有效的学生参与和人员培训。向公共和执法机构的演讲将有助于塑造对安全事件的根本原因分析并确定攻击的未来实践。该项目将导致一个整体框架，以研究在多主体动态系统中出现的安全问题的数学基础。与关注安全问题的特定方面的现有框架相比，该框架将是一个重大进步。该项目将推进游戏理论和计算中的多个子场。首先，它将结合多代理安全游戏和学习技术，为利用环境不确定性的攻击提供新颖的解决方案。其次，这项工作将基于随机算法的进度，以解决多代理游戏中引起的大型优化问题。该方法将为强大的多标准优化和多代理学习提供新的概率范式。它将表征系统的攻击弹性与所涉及的计算之间的权衡。第三，包括使用自动驾驶汽车测试的真实流动性的分层评估将有助于将该框架过渡到现实世界中的应用。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响审查标准通过评估而被视为珍贵的支持。