Adversarial Attacks on Deep Neural Network-based Power System Event Classification Models
Adversarial Attacks on Deep Neural Network-based Power System Event Classification Models
复制标题
基于深度神经网络的电力系统事件分类模型的对抗性攻击
DOI：
10.1109/isgtasia54193.2022.10003611
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
N. Yu
中科院分区：
文献类型：
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作者：
Yuanbin Cheng;Koji Yamashita;N. Yu
在线事件分类对于加强输电系统的可靠性至关重要。最近，基于深度学习的方法在计算机视觉和自然语言处理等众多领域取得了巨大成功。研究人员开始采用基于深度学习的方法来解决电力系统事件识别问题，并取得了有效的成果。然而，这些以前的工作没有考虑到深度学习模型容易受到对抗性攻击，可能会影响现实世界应用程序的可靠性。在本文中，我们采用了几种对抗性攻击机制，通过向输入相量测量单元（PMU）时间序列中添加定制的噪声信号，使深度学习模型对电力系统事件进行错误分类。这项数值研究表明，目前最先进的基于深度学习的电力系统事件分类器非常容易受到对抗性攻击，这可能会危及电力传输系统的可靠性。
Online event classification is essential to strengthening the reliability of the power transmission system. Recently, deep learning based methods have achieved great success in numerous domains such as computer vision and natural language processing. Researchers began to adopt deep learning based methods to solve the power system event identification problem and achieved effective results. However, these previous works do not consider that deep learning models are vulnerable to adversarial attacks, potentially influencing real-world applications' reliability. In this paper, we adopt several adversarial attack mechanisms by adding tailored noise signal to the input Phasor Measurement Units (PMU) time series and make the deep learning model misclassify the power system event. This numerical study discloses that current state-of-the-art deep learning based power system event classifiers are extremely vulnerable to adversarial attacks, which may jeopardize the reliability of the power transmission system.