Cyber-Attack Classification in Smart Grid via Deep Neural Network
Cyber-Attack Classification in Smart Grid via Deep Neural Network
复制标题
通过深度神经网络对智能电网中的网络攻击进行分类
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Tianchen Zhang
中科院分区：
文献类型：
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作者：
Liang Zhou;Ouyang Xuan;H. Ying;Lifang Han;Yushi Cheng;Tianchen Zhang
智能电网1是一种现代化的输电网络。随着计算机技术的发展，计算、通信和物理过程之间的联系越来越紧密。然而，对手可以通过攻击电力二次设备来破坏电力生产。准确、快速地应对网络攻击是电网稳定运行的前提。因此，识别和分类智能电网中的攻击至关重要。在本文中，我们提出了一种新的方法，利用机器学习算法来帮助分类网络攻击。我们建立了一个深度神经网络（DNN）模型，并选择全局最优参数以实现高泛化性能。评估结果表明，该方法可以有效地识别智能电网中的网络攻击，准确率高达96%。
Smart grid1 is a modern power transmission network. With its development, the computing, communication and physical processes is getting more and more connected. However, an adversary can destroy power production by attacking the power secondary equipment. Accurate and fast response to cyber-attacks is a prerequisite for stable grid operation. Therefore, it is critical to identify and classify attacks in the smart grid. In this paper, we propose a novel approach that utilizes machine learning algorithms to help classify cyber-attacks. We built a deep neural network (DNN) model and select the global optimal parameters to achieve high generalization performance. The evaluation result demonstrates that the proposed method can effectively identify cyber-attacks in smart grid with an accuracy as high as 96%.