CPS: Synergy: Collaborative Research: Cyber-Physical Approaches to Advanced Manufacturing Security

制造系统从网络和物理组件的松散集合演变为真正的网络-物理系统，扩大了针对制造业的网络攻击的机会。为了确保在这种新环境中继续生产高质量的部件，需要开发超越网络和物理世界的新型安全工具。潜在的网络攻击可能会导致制造系统中无法检测到的变化，从而对产品的设计意图、性能、质量或感知质量产生不利影响。这样做的结果可能会导致产品发布延迟、设备损坏、保修成本增加或失去客户信任，从而造成经济损失。更重要的是，这些攻击对人类安全构成了威胁，因为运营商和消费者可能正在使用有缺陷的设备/产品。检测和诊断网络物理攻击的新方法将通过我们已建立的工业伙伴进行研究和评估。该项目的预期结果将为进一步保护我国的制造业基础设施作出重大贡献。该项目基于建模和理解产品/工艺开发周期中发生的网络事件与制造过程中生成的物理数据之间的相关性，为制造网络安全建立了新的愿景。具体而言，拟议的研究将利用这种相关性来表征网络攻击、过程数据、产品质量观察和侧通道影响之间的关系，以进行攻击检测和诊断。这些过程特征将与新的制造特定的网络攻击分类法相结合，以提供对先进制造系统的攻击面及其在制造过程中的网络物理表现的全面理解。这是制造业网络安全知识体系中缺失的一个基本要素。最后，将研究基于约束优化和机器学习的新取证技术，以区分表明网络攻击的过程变化与由于固有系统可变性而导致的制造过程中的常见变化。

The evolution of manufacturing systems from loose collections of cyber and physical components into true cyber-physical systems has expanded the opportunities for cyber-attacks against manufacturing. To ensure the continued production of high-quality parts in this new environment requires the development of novel security tools that transcend both the cyber and physical worlds. Potential cyber-attacks can cause undetectable changes in a manufacturing system that can adversely affect the product's design intent, performance, quality, or perceived quality. The result of this could be financially devastating by delaying a product's launch, ruining equipment, increasing warranty costs, or losing customer trust. More importantly, these attacks pose a risk to human safety, as operators and consumers could be using faulty equipment/products. New methods for detecting and diagnosing cyber-physical attacks will be studied and evaluated through our established industrial partners. The expected results of this project will contribute significantly in further securing our nation's manufacturing infrastructure.This project establishes a new vision for manufacturing cyber-security based upon modeling and understanding the correlation between cyber events that occur in a product/process development-cycle and the physical data generated during manufacturing. Specifically, the proposed research will take advantage of this correlation to characterize the relationships between cyber-attacks, process data, product quality observations, and side-channel impacts for the purpose of attack detection and diagnosis. These process characterizations will be coupled with new manufacturing specific cyber-attack taxonomies to provide a comprehensive understanding of attack surfaces for advanced manufacturing systems and their cyber-physical manifestations in manufacturing processes. This is a fundamental missing element in the manufacturing cyber-security body of knowledge. Finally, new forensic techniques, based on constraint optimization and machine learning, will be researched to differentiate process changes indicative of cyber-attacks from common variations in manufacturing due to inherent system variability.