Vulnerability Analysis and Consequences of False Data Injection Attack on Power System State Estimation

TL;DR

This paper investigates the physical impacts of false data injection attacks on power system state estimation, demonstrating how attackers can overload lines by manipulating measurements within realistic AC models.

Contribution

It introduces a bi-level optimization framework to analyze the physical consequences of FDI attacks on AC power systems, bridging the gap between cyber attacks and physical system effects.

Findings

Attackers can overload transmission lines with moderate load shifts.

Bi-level optimization effectively models attack strategies and consequences.

Realistic AC models confirm vulnerabilities in power system security.

Abstract

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.