Targeted False Data Injection Attacks Against AC State Estimation Without Network Parameters

TL;DR

This paper introduces a novel false data injection attack model against AC state estimation that requires only limited PMU data and can target specific states to bypass detection.

Contribution

The paper presents a new FDIA method that extracts network parameters from PMU data without prior network knowledge, enabling targeted attacks on AC state estimation.

Findings

Successfully bypasses bad data detection in IEEE 39-bus system

Can target specific states with high probability

Effective even with limited PMU data

Abstract

State estimation is a data processing algorithm for converting redundant meter measurements and other information into an estimate of the state of a power system. Relying heavily on meter measurements, state estimation has proven to be vulnerable to cyber attacks. In this paper, a novel targeted false data injection attack (FDIA) model against AC state estimation is proposed. Leveraging on the intrinsic load dynamics in ambient conditions and important properties of the Ornstein-Uhlenbeck process, we, from the viewpoint of intruders, design an algorithm to extract power network parameters purely from PMU data, which are further used to construct the FDIA vector. Requiring no network parameters and relying only on limited phasor measurement unit (PMU) data, the proposed FDIA model can target specific states and launch large deviation attacks. Sufficient conditions for the proposed FDIA model are also developed. Various attack vectors and attacking regions are studied in the IEEE 39-bus system, showing that the proposed FDIA method can successfully bypass the bad data detection and launch targeted large deviation attacks with very high probabilities.