Detection of False Data Injection Attacks in Smart Grid under Colored Gaussian Noise

TL;DR

This paper introduces a generalized likelihood ratio test for detecting false data injection attacks in smart grids with colored Gaussian noise, improving detection accuracy over traditional methods.

Contribution

The paper develops a novel GLRT-based detector that accounts for colored Gaussian noise, extending the applicability beyond conventional Gaussian noise assumptions.

Findings

The proposed detector outperforms traditional Gaussian noise-based detectors.

Effective detection of both observable and unobservable false data attacks.

Validated on IEEE 30-bus system with superior results.

Abstract

In this paper, we consider the problems of state estimation and false data injection detection in smart grid when the measurements are corrupted by colored Gaussian noise. By modeling the noise with the autoregressive process, we estimate the state of the power transmission networks and develop a generalized likelihood ratio test (GLRT) detector for the detection of false data injection attacks. We show that the conventional approach with the assumption of Gaussian noise is a special case of the proposed method, and thus the new approach has more applicability. {The proposed detector is also tested on an independent component analysis (ICA) based unobservable false data attack scheme that utilizes similar assumptions of sample observation.} We evaluate the performance of the proposed state estimator and attack detector on the IEEE 30-bus power system with comparison to conventional Gaussian noise based detector. The superior performance of {both observable and unobservable false data attacks} demonstrates the effectiveness of the proposed approach and indicates a wide application on the power signal processing.