CPS:Small:Enhancing Cybersecurity of Chemical Process Control Systems

智能制造使用旨在提高利润和减少资源使用的算法，同时减少人为错误，使制造过程变得越来越自动化，预计将提高化学反应、分离和运输重要的行业的生产效率。通信和自动化水平的提高也在影响其他涉及分子水平过程控制的行业，如医疗保健、水处理和灌溉。然而，加强这些过程自动化的一个挑战是防止对执行通信和计算以实现自动化的系统(称为控制系统)进行网络攻击。如果对控制系统的网络攻击成功，可能会影响安全、盈利能力或生产量等因素。尽管对安全至关重要的行业有许多防御措施，试图防止攻击者造成伤害，但一个悬而未决的问题是，如何设计出更强大的保障措施，防止对自动化系统的成功攻击。这项工作旨在开发先进控制算法的基本进展，该算法与检测网络攻击和提醒公司人员注意由复杂动态模型描述的化学过程的存在的算法相结合。本项目旨在描述过程自动化算法对自动化系统(例如，传感器和执行器)各个方面的网络攻击具有弹性的条件，即从安全的角度来看，攻击不能成功地产生有问题的过程行为，即使它们违反了某些信息技术防御措施。该项目还将开发一些算法，以提高下一代制造业的安全性和效率，并探索网络攻击可能如何影响这些算法。为了广泛传播这些主题的信息，将开发一个通过YouTube共享的现场动作和动画短视频，其中角色生活的情节和世界通过故事向观众展示本研究所追求的控制、网络安全和工程概念。计划中的研究计划将全面评估涉及不同类型分子水平现象的过程的网络攻击特征，并将通过与检测算法等其他框架集成的控制设计，开发控制理论和算法的基本进展，以增强这些过程的网络安全。将描述通过拟议的发展加强网络安全的理论条件(即攻击不会对进程造成安全问题)。具体而言，将涉及以下问题：a)将制定各种化学过程不同类型“不良行为”定义的数学正规化，并澄清不同化学过程系统的合理类型的网络攻击；b)控制和状态估计设计将与检测技术相结合，以便在网络攻击即使穿透某些信息技术防御系统也不能产生不良行为的条件下制定保证；c)将开发网络物理系统的新型传感和控制能力，利用机器学习和数学增加化学过程的灵活性，并调查如何对这些进展进行网络攻击；D)将开发通过物理手段(例如，材料/设备设计和选择)了解和防止网络攻击期间不良行为的技术；以及e)将在各种行业的化学过程的背景下展示和评估这些发展。这些进展将集中在模型预测控制下由非线性动态模型描述的过程，但也将扩展到其他类型的过程(例如，一类随机微分方程或偏微分方程)。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Smart manufacturing, in which manufacturing processes become increasingly automated using algorithms intended to boost profits and reduce resource use while decreasing human error, is expected to enhance production efficiency in industries where chemical reaction, separation, and transport are important. Heightened communication and automation are also impacting other industries that involve control of molecular-level processes, as in healthcare, water treatment, and irrigation. However, a challenge for enhanced automation of these processes is preventing cyberattacks on the systems (referred to as control systems) that perform communication and computation to enable automation. If a cyberattack on a control system succeeds, it may impact factors such as safety, profitability, or production volume. Though safety-critical industries have many defenses in place to seek to prevent attackers from causing harm, an open question is how to design stronger safeguards against successful attacks into automation systems. This work aims to develop fundamental advances in advanced control algorithms integrated with algorithms for detecting cyberattacks and alerting company personnel to their presence for chemical processes described by complex dynamic models. This project seeks to characterize the conditions under which the process automation algorithms can be made resilient to cyberattacks on various aspects of the automation systems (e.g., sensors and actuators) in the sense that attacks cannot succeed at creating problematic process behavior from a safety standpoint even if they breach certain information technology defenses. The project will also pursue the development of a number of algorithms for enhancing safety and efficiency for next-generation manufacturing, and explore how cyberattacks may impact these. To disseminate information on these topics broadly, a live action and an animated short video to be shared via YouTube will be developed, in which the plot and the world in which the characters live expose viewers to the concepts of control, cybersecurity, and engineering pursued in this research through story.The planned research program will comprehensively evaluate the characteristics of cyberattacks for processes involving molecular-level phenomena of different types, and will develop fundamental advances in control theory and algorithms for enhancing cybersecurity for these processes through control designs integrated with other frameworks such as detection algorithms. The theoretical conditions under which cybersecurity is enhanced by the proposed developments (in the sense that the attacks cannot create a safety issue for the process) will be characterized. Specifically, the following will be addressed: a) a mathematical formalization of the definition of different types of "undesirable behavior" for various chemical processes and clarification of reasonable types of cyberattacks for different chemical process systems will be developed; b) control and state estimation designs will be combined with detection techniques to allow guarantees to be developed on the conditions under which a cyberattack cannot create undesirable behavior even if it penetrates certain information technology defenses; c) novel sensing and control capabilities for cyber-physical systems will be developed that take advantage of machine learning and mathematics to increase flexibility of chemical processes, with investigations of how these advances may be cyberattacked; d) techniques for understanding and preventing undesirable behavior during a cyberattack through physical means (e.g., materials/equipment design and selection) will be developed; and e) the developments will be demonstrated and evaluated within the context of chemical processes across a variety of industries. These developments will focus on processes described by nonlinear dynamic models under model predictive control, but will also make extensions to processes of other types (e.g., a class of stochastic differential equations or partial differential equations).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.