论文标题
无限脉冲响应图形神经网络用于网络攻击的智能网格本地化
Infinite Impulse Response Graph Neural Networks for Cyberattack Localization in Smart Grids
论文作者
论文摘要
这项研究采用无限脉冲响应（IIR）图神经网络（GNN）有效地对智能网格数据的固有图形网络结构进行建模，以解决网络攻击本地化问题。首先，我们通过数值分析有限脉冲响应（FIR）和IIR图过滤器（GFS）的经验频率响应，以近似理想的光谱响应。我们表明，对于相同的滤波器顺序，IIR GF可以更好地近似所需的光谱响应，并且由于其合理类型的滤镜响应，它们也与较低阶GF的近似值相同。其次，我们提出了一个IIR GNN模型，以有效预测总线上的网络攻击的存在。最后，我们在样本（SW）和BUS（BW）水平的各种网络攻击下评估了模型，并将结果与现有体系结构进行比较。经过实验验证的是，所提出的模型的表现分别优于最先进的FIR GNN模型，分别在SW和BW定位方面均优于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.
