Infinite Impulse Response Graph Neural Networks for Cyberattack Localization in Smart Grids

TL;DR

This paper introduces an IIR graph neural network approach for cyberattack localization in smart grids, demonstrating superior spectral approximation and improved localization accuracy over FIR-based models.

Contribution

The paper proposes a novel IIR GNN model that better approximates spectral responses and enhances cyberattack localization accuracy in smart grids.

Findings

IIR GFs outperform FIR GFs in spectral approximation.

The proposed IIR GNN improves localization accuracy by up to 14%.

Model outperforms existing architectures in cyberattack detection.

Abstract

This study employs Infinite Impulse Response (IIR) Graph Neural Networks (GNN) to efficiently model the inherent graph network structure of the smart grid data to address the cyberattack localization problem. First, we numerically analyze the empirical frequency response of the Finite Impulse Response (FIR) and IIR graph filters (GFs) to approximate an ideal spectral response. We show that, for the same filter order, IIR GFs provide a better approximation to the desired spectral response and they also present the same level of approximation to a lower order GF due to their rational type filter response. Second, we propose an IIR GNN model to efficiently predict the presence of cyberattacks at the bus level. Finally, we evaluate the model under various cyberattacks at both sample-wise (SW) and bus-wise (BW) level, and compare the results with the existing architectures. It is experimentally verified that the proposed model outperforms the state-of-the-art FIR GNN model by 9.2% and 14% in terms of SW and BW localization, respectively.