Cyber-Physical Systems (CPS) have gained popularity due to the increased requirements on their uninterrupted connectivity and process automation. Due to their connectivity over the network including intranet and internet, dependence on sensitive data, heterogeneous nature, and large-scale deployment, they are highly vulnerable to cyber-attacks. Cyber-attacks are performed by creating anomalies in the normal operation of the systems with a goal either to disrupt the operation or destroy the system completely. The study proposed here focuses on detecting those anomalies which could be the cause of cyber-attacks. This is achieved by deriving the rules that govern the physical behavior of a process within a plant. These rules are called Invariants. We have proposed a Data-Centric approach (DaC) to generate such invariants. The entire study was conducted using the operational data of a functional smart power grid which is also a living lab.
翻译:由于对网络物理系统(CPS)的不间断连通和流程自动化要求增加,网络物理系统(CPS)越来越受欢迎。由于这些系统在互联网和内联网等网络上的连通性、对敏感数据的依赖性、多样性性质和大规模部署,它们极易受到网络攻击的伤害。网络攻击是通过在系统正常运行中制造异常来进行的,目的是干扰运行或彻底摧毁系统。本研究报告建议的研究侧重于发现那些可能成为网络攻击原因的异常现象。这是通过制定管理一个工厂内程序物理行为的规则来实现的。这些规则被称为“异性规则 ” 。我们建议了一种数据中心方法来产生这种变异性。整个研究使用了功能智能电网的操作数据,该电网也是一个活的实验室。