Infinite Impulse Response Graph Neural Networks for Cyberattack Localization in Smart Grids Institutional Repository Document uri icon

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.

author list (cited authors)

  • Boyaci, O., Narimani, M. R., Davis, K., & Serpedin, E.

citation count

  • 0

complete list of authors

  • Boyaci, Osman||Narimani, M Rasoul||Davis, Katherine||Serpedin, Erchin

Book Title

  • arXiv

publication date

  • June 2022