Infinite Impulse Response Graph Neural Networks for Cyberattack Localization in Smart Grids
Infinite Impulse Response Graph Neural Networks for Cyberattack Localization in Smart Grids
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DOI：
10.48550/arxiv.2206.12527
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Osman Boyaci;M. Narimani;K. Davis;E. Serpedin
中科院分区：
文献类型：
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作者：
Osman Boyaci;M. Narimani;K. Davis;E. Serpedin
本研究采用无限脉冲响应（IIR）图形神经网络（GNN）来有效地建模智能电网数据的固有图形网络结构，以解决网络攻击定位问题。首先，我们数值分析有限脉冲响应（FIR）和IIR图形滤波器（GF）的经验频率响应，以近似理想的频谱响应。我们表明，对于相同的滤波器阶数，IIR GF提供了一个更好的近似所需的光谱响应，他们也提出了相同的水平的近似低阶GF由于其合理的类型的滤波器响应。其次，我们提出了一个IIR GNN模型来有效地预测总线级别的网络攻击。最后，我们评估模型在各种网络攻击下的样本明智（SW）和总线明智（BW）的水平，并与现有的架构的结果进行比较。实验证明，该模型在SW和BW定位方面分别比最先进的FIR GNN模型高出9.2%和14%。
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.