CAREER: Understanding the Integrated Cyber-Physical Resilience of Continuous Critical Manufacturing

工业物联网（IIoT）技术引发了人们对制造业安全性和弹性的日益关注。然而，目前的解决方案缺乏对复杂系统中网络物理弹性的整体理解，未能将工业物联网网络漏洞与动态制造过程联系起来，以进行有效的检测和控制。为了解决这些差距，该学院早期职业发展（Career）项目旨在开发集成建模、检测和控制措施的新方法，以了解连续关键制造系统的网络物理弹性。这项研究将致力于消除为工业物联网在制造业中的应用制定新政策、法规和标准的障碍。通过与行业利益相关者合作，该项目承诺将其方法和工具扩展到其他关键基础设施，从而提高国家网络物理弹性，从而带来长期效益。同时，这个项目的教育和推广活动在未来和现在的制造业专业人员中培养了对网络物理弹性的持续意识。引入新的课程和培训材料可以增强学生对先进制造技术的了解，提高他们的数据科学和网络安全技能。K-12外展计划促进了对工业物联网和网络物理弹性的理解，促进了制造业的发展。专门设计的培训软件解决了直观的工程语言网络安全培训的需要。这些努力与国家先进制造战略相一致，有助于确保国家安全的目标。本研究解决了连续制造系统的网络物理弹性中的关键挑战。研究目标包括：(1)可推广工具的开发：本项目旨在建立可推广的网络物理弹性量化工具。通过创建集成网络连通性和异构组件的系统动力学的随机模型，将建立一个新的量化度量。该指标通过随机优化考虑了工业物联网网络特征和制造系统动态，揭示了工业物联网连接引起的系统级风险。(2)重新思考异常检测：该项目将通过将系统范围的弹性量化纳入基于过程的异常检测算法，重新思考网络物理弹性驱动的异常检测。这包括设计包含弹性的新型半监督学习算法，重点是理解在高维数据驱动的异常检测中控制检测精度和弹性增强的理论。(3)基于协作学习的弹性控制策略：本研究旨在创建基于协作学习的弹性控制策略。利用强化学习和系统连接，这些策略增强了系统对网络攻击的适应性。这涉及探索垂直联合强化学习的未被探索的领域，并产生关于单个机器的控制性能和系统对对手的适应性之间权衡的新知识。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。

Industrial internet-of-things (IIoT) technologies spark growing interest in manufacturing security and resilience. However, current solutions lack a holistic understanding of cyber-physical resilience in complex systems, failing to connect IIoT network vulnerabilities with dynamic manufacturing processes for effective detection and control. To address these gaps, this Faculty Early Career Development (CAREER) project aims to develop novel methodologies that integrate modeling, detection, and control measures for understanding the cyber-physical resilience of continuous critical manufacturing systems. This study will work to eliminate barriers to the development of new policies, regulations, and standards for IIoT applications in manufacturing. In collaboration with industry stakeholders, this project promises long-term benefits by extending its methods and tools to other critical infrastructures, thereby enhancing national cyber-physical resilience. Meanwhile, the education and outreach activities in this project foster sustained awareness of cyber-physical resilience among both future and current manufacturing professionals. Introducing new courses and training materials enhances students' exposure to advanced manufacturing technologies and improves their data science and cybersecurity skills. K-12 outreach initiatives boost understanding of IIoT and cyber-physical resilience, promoting manufacturing careers. A specially designed training software addresses the need for intuitive cybersecurity training in engineering language. These endeavors align with the National Strategy for Advanced Manufacturing by contributing to the goal of ensuring national security.This study addresses critical challenges in continuous manufacturing systems' cyber-physical resilience. The research objectives include (1) Development of Generalizable Tools: The project aims to build generalizable tools for cyber-physical resilience quantification. By creating stochastic models that integrate cyber connectivity and system dynamics of heterogeneous components, a novel quantification metric will be established. This metric considers both IIoT network features and manufacturing system dynamics through stochastic optimization, revealing system-level risks induced by IIoT connectivity. (2) Rethinking Anomaly Detection: The project will rethink cyber-physical resilience-driven anomaly detection by incorporating system-wide resilience quantification into process-based anomaly detection algorithms. This involves designing novel semi-supervised learning algorithms that incorporate resilience, with a focus on understanding the theories governing detection accuracy and resilience enhancement in high-dimensional data-driven anomaly detection. (3) Collaborative Learning-Based Resilient Control Strategies: The study aims to create collaborative learning-based resilient control strategies. Leveraging reinforcement learning and system connectivity, these strategies enhance a system's adaptability to cyberattacks. This involves exploring the under-explored area of vertical federated reinforcement learning and generating new knowledge regarding the trade-off between the control performance of individual machines and the system's adaptability to adversaries.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.