CPS: TTP Option: Medium: Collaborative Research: Trusted CPS from Untrusted Components

CPS：TTP 选项：中：协作研究：来自不受信任组件的受信任 CPS

• 批准号: 1837352
• 负责人: Sunil Prabhakar
• 金额: $ 3.72万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1837352&HistoricalAwards=false
• 关键词: CPS; TTP; Option; Medium; Collaborative

The nation's critical infrastructures are increasingly dependent on systems that use computers to control vital physical components, including water supplies, the electric grid, airline systems, and medical devices. These are all examples of Cyber-Physical Systems (CPS) that are vulnerable to attack through their computer systems, through their physical properties such as power flow, water flow, chemistry, etc., or through both. The potential consequences of such compromised systems include financial disaster, civil disorder, even the loss of life. The proposed work significantly advances the science of protecting CPS by ensuring that the systems "do what they are supposed to do" despite an attacker trying to make them fail or do harm. In this convergent approach, the key is to tell the CPS how it is supposed to behave and build in defenses that make sure each component behaves and works well with others. The proposed work has a clear transition to industrial practice. It will also enhance education and opportunity by opening up securing society as a fascinating discipline for K-12 students to follow. The objective of the proposed project is to produce, from untrusted components, a trusted Cyber-physical system (CPS) that is resilient to security attacks and failures. The approach will rely on information flows in both the cyber and physical subsystems, and will be validated experimentally on high fidelity water treatment and electric power CPS testbeds. The project brings together concepts from distributed computing, control theory, machine learning, and estimation theory to synthesize a complete mitigation of the security and operational threats to a CPS. The proposed method's key difference from current methods is that security holes will be identified and plugged automatically at system design time, then enforced during runtime without relying solely on secure boundaries or firewalls. The system will feature the ability to identify and isolate a malfunctioning device or cyber-physical intrusion in real-time by validating its operation against fundamental scientific/engineering principles and learned behavior. A combined mathematical/data science approach will be used to generate governing invariants that are enforced at system runtime. Invariants are a scientific approach grounded in the system's physics coupled with machine learning and real-time scheduling approaches embedded in the CPS. Robust state estimation will account for errors in measurement and automated security domain construction and optimization to reduce the cost of evaluation without sacrificing coverage. The successful outcome of this research will lead to improved national security across various CPS infrastructures which, in turn, will improve economic and population health and security. The work can be taken to industry for deployment in critical infrastructures. The project will stimulate interest in Science, Technology, Engineering and Mathematics (STEM) through the development of a water-themed tabletop exercise for K-12 and helping current college students develop an interest in outreach through the experiential learning aspects of developing the tabletop exercise.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.

国家的关键基础设施越来越依赖于使用计算机控制重要物理组件的系统，包括供水，电网，航空系统和医疗设备。 这些都是网络物理系统（CPS）的例子，这些系统容易受到通过其计算机系统，通过其物理属性（如电力流，水流，化学等）的攻击，或者两者都有。这种系统受损的潜在后果包括金融灾难、社会混乱，甚至生命损失。 拟议的工作通过确保系统“做它们应该做的事情”，大大推进了保护CPS的科学，尽管攻击者试图使它们失败或造成伤害。 在这种融合的方法中，关键是告诉CPS它应该如何行为，并建立防御机制，确保每个组件都能正常工作，并与其他组件一起工作。 拟议的工作有一个明确的过渡到工业实践。 它还将通过开放安全社会作为K-12学生遵循的迷人学科来加强教育和机会。拟议项目的目标是从不可信的组件中产生一个可信的网络物理系统（CPS），该系统能够抵御安全攻击和故障。该方法将依赖于网络和物理子系统中的信息流，并将在高保真水处理和电力CPS试验台上进行实验验证。该项目汇集了分布式计算，控制理论，机器学习和估计理论的概念，以综合全面缓解CPS的安全和运营威胁。所提出的方法与当前方法的关键区别在于，安全漏洞将在系统设计时自动识别和堵塞，然后在运行时强制执行，而不仅仅依赖于安全边界或防火墙。该系统将具有识别和隔离故障设备或网络物理入侵的能力，通过根据基本科学/工程原理和学习行为验证其操作。 将使用组合的数学/数据科学方法来生成在系统运行时强制执行的治理不变量。 不变量是一种基于系统物理的科学方法，结合了CPS中嵌入的机器学习和实时调度方法。 鲁棒的状态估计将考虑测量和自动安全域构建和优化中的错误，以在不牺牲覆盖范围的情况下降低评估成本。这项研究的成功结果将改善各种CPS基础设施的国家安全，从而改善经济和人口健康与安全。 这项工作可以在工业上部署在关键的基础设施中。 该项目将激发人们对科学，技术，工程和数学（STEM）通过开发一个以水为主题的桌面练习，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准。