Vulnerability Analysis and Consequences of False Data Injection Attack on Power System State Estimation
This addresses security vulnerabilities in power grids, with incremental improvements in attack modeling.
The paper tackles the problem of false data injection attacks on power system state estimation by introducing a bi-level optimization to maximize line overloads, and shows that attackers can overload transmission lines with moderate load shifts in realistic non-linear models.
An unobservable false data injection (FDI) attack on AC state estimation (SE) is introduced and its consequences on the physical system are studied. With a focus on understanding the physical consequences of FDI attacks, a bi-level optimization problem is introduced whose objective is to maximize the physical line flows subsequent to an FDI attack on DC SE. The maximization is subject to constraints on both attacker resources (size of attack) and attack detection (limiting load shifts) as well as those required by DC optimal power flow (OPF) following SE. The resulting attacks are tested on a more realistic non-linear system model using AC state estimation and ACOPF, and it is shown that, with an appropriately chosen sub-network, the attacker can overload transmission lines with moderate shifts of load.