Multi-Objective False Data Injection Attacks of Cyber-Physical Power Systems

In cyber-physical power systems (CPPSs), false data injection attack (FDIA) has drawn much attention due to its stealthiness. It is of great importance to investigate the potential behaviors of attackers to improve the cyber-security of CPPSs. However, most FDIA models are often constructed separately on the effect of attackers or the impact of attacks. Accordingly, this brief proposes a multi-objective stealthy FDIA scheme in AC grid model. The attack model is described as a multi-objective optimization problem, where minimization of contaminated measurements and maximization of the attack impact are considered as two objectives while remaining stealthy. To deal with the established attack model, a non-dominated sorting genetic algorithm II (NSGAII) is introduced as the solver. To improve the efficiency of generating attack vector, a new representation mechanism is proposed to describe locations and values of injected states. Additionally, during the evolutionary process, we propose a mutation operation to balance the sparsity and impact of FDIA. Simulation results on the IEEE 14-bus, 30-bus, and 118-bus systems demonstrate the feasibility of the NSGAII-based FDIA model.