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

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