论文标题
使用自动编码器方法检测错误数据注射攻击
Detection of False Data Injection Attacks Using the Autoencoder Approach
论文作者
论文摘要
国家估计对于电力系统的操作和控制具有相当大的意义。但是，精心设计的虚假数据注射攻击可以利用常规基于残差的不良数据检测方法中的盲点来以协调的方式操纵测量，从而影响网格的安全操作和经济调度。在本文中，我们提出了一种基于自动编码器神经网络的检测方法。通过在“正常”操作数据中培训网络的依赖性固有性，它有效地克服了电力系统攻击检测中固有的不平衡训练数据的挑战。为了评估所提出机制的检测性能，我们在IEEE 118-BUS功率系统上进行了一系列实验。该实验表明，所提出的自动编码器检测器在各种攻击方案下显示出强大的检测性能。
State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in 'normal' operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.
