Resilience of Industrial Control Systems Using Signal Temporal Logic And Autotuning Mechanism
Resilience of Industrial Control Systems Using Signal Temporal Logic And Autotuning Mechanism
复制标题
DOI：
10.1109/dasc/picom/cbdcom/cy59711.2023.10361314
复制
发表时间：
2023-11
期刊：
影响因子：
--
通讯作者：
C. Agbo;Hoda Mehrpouyan
中科院分区：
文献类型：
--
作者：
C. Agbo;Hoda Mehrpouyan
构建强大、可靠和有弹性的安全关键系统是当今国家和地区面临的重大挑战。原因是这些系统的复杂性和技术先进性及其远程控制引发了各种安全威胁，导致多年来成功攻击的数量大幅增加。为了应对这些挑战，需要及时检测和恢复受到攻击或故障的系统。为此，我们提出了一个STL自动调优框架，它集成了信号时序逻辑（STL）形式主义和自动调优机制的概念，以推理系统的弹性。在这项工作中，我们专注于关键过程的研究，其变化对整个系统有显着的影响。我们的基于STL的方法在违规情况下实施，可以实时监控这些流程，以便及时检测能够破坏系统的违规行为。自动调整机制采用简单内模控制（SIMC）PID规则，用于在攻击或故障下快速恢复系统。我们实现了一个STL定量语义来衡量触发自动调优机制所需的系统安全约束的违反程度。原因是为了避免在不影响系统安全性和可用性的轻微违规时触发自动调优机制。我们测试了我们提出的方法在田纳西州伊士曼工厂，一个复杂的模型明确设计的工业过程和控制的研究。从我们的实验中得出的结果表明，我们的方法在及时检测违规行为和实现及时有效的工厂恢复的有效性。因此，我们的框架非常适合构建健壮，可靠和有弹性的系统。
Building robust, reliable, and resilient safety critical systems is a major challenge facing nations and states today. The reason is that the complexity and technological advancement of these systems and their control from remote locations have raised various security threats, leading to a high increase in successful attacks witnessed over the years. To address these challenges, timely detection and recovery of the system under attacks or failures are needed. To this end, we proposed an STL-Autotuning framework that integrates the concept of Signal Temporal Logic (STL) formalism and an autotuning mechanism to reason about system resilience. In this work, we focus on the study of critical processes whose changes have notable impacts on the overall system. Our STL-based approach implemented in breach provides real-time monitoring of these processes for the timely detection of violations capable of crippling the system. The auto-tuning mechanism adopts a Simple Internal Model Control (SIMC) PID rule for the quick recovery of the system under attacks or faults. We implemented an STL quantitative semantics to measure the degree of violations of system safety constraints necessary for triggering the autotuning mechanism. The reason is to avoid triggering the autotuning mechanism for minor violations that do not impact the system's safety and availability. We tested our proposed approach at the Tennessee Eastman Plant, a complex model explicitly designed for the study of industrial processes and control. The findings derived from our experiment demonstrate the efficacy of our approach in promptly detecting violations and effectuating timely and efficient plant recovery. As a result, our framework is highly suitable for building robust, reliable, and resilient systems.