Differential Evolution-Based Three Stage Dynamic Cyber-Attack of Cyber-Physical Power Systems
Kang‐Di Lu, Zheng‐Guang Wu, Tingwen Huang
Abstract
With the rapid development of communication, control, and computer technology, traditional power systems have evolved into cyber-physical power system (CPPS). However, CPPS not only affords convenience but also introduces more cyber-attacks. Among many types of cyber-attacks, false data injection attacks (FDIAs) have drawn much attention in the CPPS security domain due to their stealthiness. Most FDIAs are based on the static model on a single snapshot and often ignore the reality of dynamically time-evolving CPPS. Accordingly, this article proposes a novel three-stage dynamic false data injection attack (DFDIA) model in CPPS by considering potential dynamic behaviors. To consider both attack location and attack amplitude, the designing DFDIA is formulated as two constrained single-objective optimization problems. Two versions of constrained differential evolution are presented as the solver to determine the attack location and optimize the attack vector for collaboratively altering the meter measurements. Then, an interval state forecasting-based countermeasure is proposed to detect the established DFDIA. In this detector, the variation bounds of state values are determined via ensemble deep learning-based state forecasting method. Finally, extensive simulation results on several IEEE bus systems demonstrate the feasibility of DFDIA and the effectiveness of the defense mechanism.