论文标题
使用移动目标防御和深度学习，在电网中进行协调的网络物理攻击定位
Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Deep Learning
论文作者
论文摘要
作为对电网最复杂的攻击之一，协调的网络物理攻击（CCPA）会损害电网的物理基础设施，并使用同时进行网络攻击来掩盖其效果。这项工作提出了一种新颖的方法来检测此类攻击并确定断路的位置（由于物理攻击）。提出的方法由三个部分组成。首先，通过通过分布式柔性交流传输系统（D-FACTS）设备积极扰动传输线电抗来实现移动目标防御（MTD）来暴露物理攻击。 MTD使攻击者掩盖其物理攻击所需的知识无效。其次，将卷积神经网络（CNN）应用于受损测量值的定位断电位置。最后，模型不可知的元学习（MAML）用于加速拓扑重构后CNN的训练速度（由于MTD），并减少数据/再培训时间要求。使用IEEE测试系统进行仿真。实验结果表明，所提出的方法可以有效地将断电在隐形的CCPA中定位。
As one of the most sophisticated attacks against power grids, coordinated cyber-physical attacks (CCPAs) damage the power grid's physical infrastructure and use a simultaneous cyber attack to mask its effect. This work proposes a novel approach to detect such attacks and identify the location of the line outages (due to the physical attack). The proposed approach consists of three parts. Firstly, moving target defense (MTD) is applied to expose the physical attack by actively perturbing transmission line reactance via distributed flexible AC transmission system (D-FACTS) devices. MTD invalidates the attackers' knowledge required to mask their physical attack. Secondly, convolution neural networks (CNNs) are applied to localize line outage position from the compromised measurements. Finally, model agnostic meta-learning (MAML) is used to accelerate the training speed of CNN following the topology reconfigurations (due to MTD) and reduce the data/retraining time requirements. Simulations are carried out using IEEE test systems. The experimental results demonstrate that the proposed approach can effectively localize line outages in stealthy CCPAs.
