Modeling and performance evaluation of stealthy false data injection attacks on smart grid in the presence of corrupted measurements

TL;DR

This paper presents a new sparse optimization method for constructing stealthy false data injection attacks on smart grids that remain undetected even with corrupted measurements, outperforming existing techniques in stealth and efficiency.

Contribution

It introduces a novel sparse optimization approach enabling blind FDI attacks to bypass detection despite measurement corruption, enhancing attack stealth and speed.

Findings

The proposed method outperforms existing techniques in stealthiness.

It remains effective even with gross measurement errors.

The approach is computationally efficient for real-time applications.

Abstract

The false data injection (FDI) attack cannot be detected by the traditional anomaly detection techniques used in the energy system state estimators. In this paper, we demonstrate how FDI attacks can be constructed blindly, i.e., without system knowledge, including topological connectivity and line reactance information. Our analysis reveals that existing FDI attacks become detectable (consequently unsuccessful) by the state estimator if the data contains grossly corrupted measurements such as device malfunction and communication errors. The proposed sparse optimization based stealthy attacks construction strategy overcomes this limitation by separating the gross errors from the measurement matrix. Extensive theoretical modeling and experimental evaluation show that the proposed technique performs more stealthily (has less relative error) and efficiently (fast enough to maintain time requirement) compared to other methods on IEEE benchmark test systems.