  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.
