CPS:Small:Enhancing Cybersecurity of Chemical Process Control Systems

智能制造，其中制造过程越来越自动化，使用旨在提高利润和减少资源使用同时减少人为错误的算法，预计将提高化学反应，分离和运输重要的行业的生产效率。通信和自动化的提高也影响了其他涉及分子级过程控制的行业，如医疗保健，水处理和灌溉。然而，这些过程的增强自动化的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）的网络攻击。如果对控制系统的网络攻击成功，可能会影响安全性、盈利能力或产量等因素。虽然安全关键型行业有许多防御措施来防止攻击者造成伤害，但一个悬而未决的问题是如何设计更强大的安全措施来防止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根本性进展，这些算法与用于检测网络攻击的算法相结合，并提醒公司人员注意由复杂动态模型描述的化学过程。该项目旨在描述过程自动化算法可以抵御自动化系统各个方面的网络攻击的条件（例如，传感器和致动器），因为从安全的角度来看，即使攻击破坏了某些信息技术防御，它们也不能成功地创建有问题的过程行为。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，并探索网络攻击如何影响这些算法。为了广泛传播关于这些主题的信息，将制作一个真人和动画短片，通过YouTube分享，其中的情节和人物生活的世界使观众了解控制、网络安全、计划中的研究计划将全面评估网络攻击的特点，涉及分子-我们将研究不同类型的水平现象，并将在控制理论和算法方面取得根本性进展，通过与检测算法等其他框架相结合的控制设计来增强这些过程的网络安全。将描述拟议发展增强网络安全的理论条件（从攻击不会对过程造成安全问题的意义上说）。具体而言，将解决以下问题：a）将开发各种化学过程的不同类型的“不良行为”的定义的数学形式化，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探测技术相结合，以保证在网络攻击无法产生的条件下得到发展不期望的行为，即使它穿透某些信息技术防御; c）将开发用于网络物理系统的新颖感测和控制能力，其利用机器学习和数学来增加化学过程的灵活性，并调查这些进步如何可能受到网络攻击; d）用于在通过物理手段的网络攻击期间理解和防止不期望的行为的技术（例如，材料/设备设计和选择）;以及e）将在各种行业的化学工艺背景下对开发进行演示和评估。这些发展将集中在模型预测控制下的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，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知识价值和更广泛的影响审查标准进行评估来支持。

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.