Cybersecurity in process control: Machine-learning detection and encrypted control

过程控制中的网络安全：机器学习检测和加密控制

• 批准号: 2227241
• 负责人: Panagiotis Christofides
• 金额: $ 30.61万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227241&HistoricalAwards=false
• 关键词: Cybersecurity; process; control; Machine; learning

The security of chemical process control systems has become crucially important since networked control systems are vulnerable to cyber-attacks. The failure to ensure cyber-security can lead to unsafe and potentially catastrophic consequences in chemical process operations, causing environmental damage, capital loss, and human injuries. In recent years, cyber-attacks have been designed by sophisticated adversaries to modify the actuator, the sensor, or the control action, yet remain undetectable by classical detection methods. Therefore, real-time detection of cyber-attacks and mitigation of their effects is an important research problem whose solution could directly impact the safety and security of the chemical process industries. Motivated by these considerations, the goal of this research program is to develop the theory and computational methods needed for detecting and handling intelligent cyber-attacks on process control systems for broad classes of nonlinear processes based on machine learning techniques and encryption tools incorporated within the framework of model-based control methods. User-friendly software will be developed and integrated into the most widely used chemical process CAD software; short courses and workshops will be created to disseminate these computational tools. Furthermore, the research results will be incorporated within the undergraduate process control and senior process design and economics course curricula at UCLA to introduce applications of machine learning techniques in accordance with departmental ABET goals as well as UCLA campus goals on the implementation of a Data Science minor. Finally, the involvement of a diverse group of undergraduate and graduate students in the research through participation in the Center for Engineering Education and Diversity at UCLA, and outreach to Community Colleges by offering summer internships to highly qualified students, will be pursued. The focus of this research program is on the design and implementation of computational methods to prevent cyber-attacks that can compromise data integrity, closed-loop stability, and process operational safety of industrial process control systems. This will be carried out by developing a data-based detection approach using machine learning algorithms to solve classification problems of cyber-attacks using time-series measurement data. Central to this approach is the design of encryption-based model predictive control (MPC) schemes that can operate the process safely and with minimal performance degradation in the presence of cyber-attacks. This will be accomplished through the development of a novel cyber-secure MPC stability-performance control architecture that can detect cyber-attacks and isolate the affected sensors and actuators in the control network while maintaining closed-loop stability. The effectiveness of the resulting cyber-security framework will be demonstrated through applications to high-fidelity, large-scale chemical process networks in an ASPEN simulation environment incorporating industrial process variability data.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.

由于网络控制系统易受网络攻击，化工过程控制系统的安全性变得至关重要。未能确保网络安全可能导致化学过程操作中的不安全和潜在的灾难性后果，造成环境破坏，资本损失和人员伤害。近年来，复杂的对手设计了网络攻击，以修改执行器，传感器或控制动作，但仍然无法通过经典的检测方法检测到。因此，实时检测网络攻击并减轻其影响是一个重要的研究问题，其解决方案可能直接影响化学加工行业的安全性。出于这些考虑，本研究计划的目标是开发检测和处理智能网络攻击所需的理论和计算方法，用于基于机器学习技术和加密工具的广泛类别的非线性过程的过程控制系统。将开发方便用户的软件，并将其纳入最广泛使用的化学工艺计算机辅助设计软件;将开设短期课程和讲习班，传播这些计算工具。此外，研究结果将被纳入加州大学洛杉矶分校的本科过程控制和高级过程设计和经济学课程，以根据部门ABET目标以及加州大学洛杉矶分校校园目标实施数据科学未成年人介绍机器学习技术的应用。最后，将通过参与加州大学洛杉矶分校工程教育和多样性中心，以及通过向高素质学生提供暑期实习机会向社区学院提供外展服务，让不同群体的本科生和研究生参与研究。该研究计划的重点是设计和实施计算方法，以防止可能损害工业过程控制系统的数据完整性，闭环稳定性和过程操作安全性的网络攻击。这将通过使用机器学习算法开发基于数据的检测方法来进行，以解决使用时间序列测量数据的网络攻击分类问题。这种方法的核心是基于预防的模型预测控制（MPC）方案的设计，该方案可以安全地操作过程，并且在存在网络攻击的情况下性能下降最小。这将通过开发一种新型的网络安全MPC稳定性能控制架构来实现，该架构可以检测网络攻击并隔离控制网络中受影响的传感器和执行器，同时保持闭环稳定性。该奖项反映了NSF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。