Cyber-Physical Systems (CPS) for Modern Digital Power Networks are important from the point of view of false data attacks or manipulations on digital devices like smart meters, phasor measurement units, intelligent electronic devices, etc. The study presents cyber-physical modeling of a power system from an attack detection and mitigation point of view. The research deals with generating attack vectors to spoof the bad data detection strategies and detect them using machine learning techniques.

In this talk, we present three problems relevant to the smart grids, which are the i) Optimal controller placement problem (planning), ii) attack vector generation problem (data attack), and iii) attack detection (protection). The work models the cyber-physical system controller placement and attack vector generation as an optimization problem and uses ensemble learning techniques to detect and classify these generated attacks. The models were tested on an IEEE 24 bus system and it was found that the approach is effective in detecting and mitigating the attacks.