Detection of false data injection attacks in smart grids based on graph signal processing

TL;DR

This paper introduces a novel graph signal processing-based method for detecting false data injection attacks in smart grids, capable of identifying attacks undetectable by traditional residual-based methods, especially under the AC power flow model.

Contribution

It develops a new attack detection technique leveraging the grid's graph structure and AC model, improving detection of sophisticated FDI attacks.

Findings

Successfully detects previously undetectable FDI attacks on IEEE 14-bus system

Effective in identifying attacks on voltage angles and magnitudes

Outperforms classical residual-based detection methods

Abstract

The smart grid combines the classical power system with information technology, leading to a cyber-physical system. In such an environment the malicious injection of data has the potential to cause severe consequences. Classical residual-based methods for bad data detection are unable to detect well designed false data injection (FDI) attacks. Moreover, most work on FDI attack detection is based on the linearized DC model of the power system and fails to detect attacks based on the AC model. The aim of this paper is to address these problems by using the graph structure of the grid and the AC power flow model. We derive an attack detection method that is able to detect previously undetectable FDI attacks. This method is based on concepts originating from graph signal processing (GSP). The proposed detection scheme calculates the graph Fourier transform of an estimated grid state and filters the graph's high-frequency components. By comparing the maximum norm of this outcome with a threshold we can detect the presence of FDI attacks. Case studies on the IEEE 14-bus system demonstrate that the proposed method is able to detect a wide range of previously undetectable attacks, both on angles and on magnitudes of the voltages.