论文标题
一种以数据为中心的方法，用于使用机器学习生成智能网格的不变性
A Data-Centric Approach to Generate Invariants for a Smart Grid Using Machine Learning
论文作者
论文摘要
由于对其不间断的连接性和过程自动化的要求增加，网络物理系统（CPS）已获得普及。由于它们在网络上的连通性，包括Intranet和Internet，对敏感数据，异质性质以及大规模部署的依赖，它们非常容易受到网络攻击的影响。网络攻击是通过在系统的正常操作中创建异常，其目标是破坏操作或完全破坏系统的目标。这里提出的研究重点是检测可能是网络攻击原因的异常情况。这是通过得出控制工厂过程中过程的身体行为的规则来实现的。这些规则称为不变。我们提出了一种以数据为中心的方法（DAC）来产生此类不变性。整个研究是使用功能性智能电网的操作数据进行的，该功能性智能电网也是生活实验室。
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.
