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

制造系统从松散的网络和物理组件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。为了确保在这种新的环境中继续生产高质量的零部件，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在制造系统中导致无法检测到的变化，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。这样做的后果可能是推迟产品的发布、损坏设备、增加保修成本或失去客户的信任，从而在财务上造成毁灭性的后果。更重要的是，这些攻击对人类安全构成了风险，因为运营商和消费者可能正在使用有问题的设备/产品。将通过我们现有的行业合作伙伴研究和评估检测和诊断网络物理攻击的新方法。该项目的预期结果将为进一步保障我国制造业基础设施的安全做出重大贡献。该项目基于对产品/过程开发周期中发生的网络事件与制造过程中产生的物理数据之间的关联进行建模和理解，为制造业网络安全建立了新的愿景。具体地说，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，以达到攻击检测和诊断的目的。这些过程特征将与新的特定于制造的网络攻击分类相结合，以全面了解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。这是制造业网络安全知识体系中一个基本缺失的元素。最后，基于约束优化和机器学习的新的取证技术将被研究，以区分表明网络攻击的过程变化与由于固有的系统变异性而导致的制造中的常见变化。

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.