Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids

TL;DR

This paper introduces a GNN-based detector for false data injection attacks in smart grids, leveraging physical topology and spatial correlations, and demonstrates superior detection performance over existing methods.

Contribution

It presents a novel GNN-based detection method that models the physical grid topology and spatial data correlations for improved FDIA detection.

Findings

GNN detector outperforms existing solutions by over 3% in F1 score.

Provides publicly accessible datasets and attack generation methodology.

Achieves scalable, real-time detection in various IEEE testbeds.

Abstract

False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids. To the best of authors' knowledge, no study has attempted to design a detector that automatically models the underlying graph topology and spatially correlated measurement data of the smart grids to better detect cyber attacks. The contributions of this paper to detect and mitigate FDIAs are twofold. First, we present a generic, localized, and stealth (unobservable) attack generation methodology and publicly accessible datasets for researchers to develop and test their algorithms. Second, we propose a Graph Neural Network (GNN) based, scalable and real-time detector of FDIAs that efficiently combines model-driven and data-driven approaches by incorporating the inherent physical connections of modern AC power grids and exploiting the spatial correlations of the measurement. It is experimentally verified by comparing the proposed GNN based detector with the currently available FDIA detectors in the literature that our algorithm outperforms the best available solutions by 3.14%, 4.25%, and 4.41% in F1 score for standard IEEE testbeds with 14, 118, and 300 buses, respectively.