Targeted Attack Synthesis for Smart Grid Vulnerability Analysis

Modern smart grids utilize advanced sensors and digital communication to manage the flow of electricity from generation source to consumption points. They also employ anomaly detection units and phasor measurement units (PMUs) for security and monitoring of grid behavior. However, as smart grids are distributed, vulnerability analysis is necessary to identify and mitigate potential security threats targeting the sensors and communication links. We propose a novel algorithm that uses measurement parameters, such as power flow or load flow, to identify the smart grid's most vulnerable operating intervals. Our methodology incorporates a Monte Carlo simulation approach to identify these intervals and deploys a deep reinforcement learning agent to generate attack vectors during the identified intervals that can compromise the grid's safety and stability in the minimum possible time, while remaining undetected by local anomaly detection units and PMUs. Our approach provides a structured methodology for effective smart grid vulnerability analysis, enabling system operators to analyze the impact of attack parameters on grid safety and stability and facilitating suitable design changes in grid topology and operational parameters.