Attack Power System State Estimation by Implicitly Learning the Underlying Models
Attack Power System State Estimation by Implicitly Learning the Underlying Models
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DOI：
10.1109/tsg.2022.3197770
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发表时间：
2023-01
影响因子：
9.6
通讯作者：
N. Costilla-Enríquez;Yang Weng
中科院分区：
文献类型：
--
作者：
N. Costilla-Enríquez;Yang Weng
由于数据采集系统的空前集成，虚假数据注入攻击（FDIA）是现代电力系统网络中一个真实的和潜在的威胁。了解攻击机制对设计对抗措施至关重要。为了成功地部署FDIA，大多数过去的FDIA策略需要特权的电力系统信息，这是由电力系统运营商仔细持有。较新的方法仅依靠截获的测量数据来规避这个问题，但它们缺乏成功的数学保证。本文揭示了电力系统的脆弱性，表明它是可能的，部署攻击没有机密信息，并在同一时间，有很高的成功概率。我们提出了一个方案，学习（1）隐含的电力系统测量分布和（2）未知的状态估计模型的代理。该框架利用Wasserstein生成对抗网络来学习测量分布，并利用自动编码器来学习未知状态估计器模型。此外，我们提出了一个收敛性证明，确保所提出的框架收敛到电力系统测量分布。通过对IEEE 9-、14-、57-、118-和300-总线测试用例的广泛仿真，证明所提出的方法是成功的。
False data injection attacks (FDIAs) are a real and latent threat in modern power systems networks due to the unprecedented integration of data acquisition systems. It is of utmost importance to understand attacking mechanisms to design countermeasures. To successfully deploy a FDIA, most past FDIA strategies need privileged power system information, which is carefully held by the power system operator. Newer approaches circumvent this issue by solely relying on intercepted measurement data, but they lack mathematical warranties of succeeding. This paper exposes power systems’ vulnerability by showing that it is possible to deploy an attack without confidential information and, at the same time, to have a high probability of being successful. We present a scheme that learns (1) the implicit power system measurement distribution and (2) a surrogate of the unknown state estimator model. The proposed framework utilizes a Wasserstein generative adversarial network to learn the measurement distribution and an autoencoder to learn the unknown state estimator model. Additionally, we present a convergence proof that ensures that the proposed framework converges to the power system measurement distribution. The proposed method is demonstrated to be successful via extensive simulation on IEEE 9-, 14-, 57-, 118-, and 300-bus test cases.