Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach
Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach
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DOI：
10.1109/tsg.2020.3010510
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发表时间：
2021-01
影响因子：
9.6
通讯作者：
Ying Zhang;Jianhui Wang;Bo Chen
中科院分区：
文献类型：
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作者：
Ying Zhang;Jianhui Wang;Bo Chen
对先进信息和通信技术的依赖增加了智能电网在网络攻击下的脆弱性。最近对不可观察的虚假数据注入攻击（FDIA）的研究揭示了安全系统操作的高风险，因为这些攻击可以绕过当前的不良数据检测机制。为了减轻这种风险，本文提出了一种基于数据驱动的学习算法，用于检测配电系统中不可观测的FDIA。我们使用自动编码器进行有效的降维和测量数据集的特征提取。此外，我们将自动编码器集成到高级生成对抗网络（GAN）框架中，该框架通过捕获异常和安全测量之间的不一致性，成功地检测FDIA下的异常。此外，考虑到从实际电力系统中收集的数据集是部分标记的，由于昂贵的标签成本和丢失的标签，所提出的方法只需要少量的标记测量数据，除了未标记的数据进行训练。在三相不平衡IEEE 13节点和123节点配电系统中的数值仿真验证了该方法的检测精度和效率。
The dependence on advanced information and communication technology increases the vulnerability in smart grids under cyber-attacks. Recent research on unobservable false data injection attacks (FDIAs) reveals the high risk of secure system operation, since these attacks can bypass current bad data detection mechanisms. To mitigate this risk, this paper proposes a data-driven learning-based algorithm for detecting unobservable FDIAs in distribution systems. We use autoencoders for efficient dimension reduction and feature extraction of measurement datasets. Further, we integrate the autoencoders into an advanced generative adversarial network (GAN) framework, which successfully detects anomalies under FDIAs by capturing the unconformity between abnormal and secure measurements. Also, considering that the datasets collected from practical power systems are partially labeled due to expensive labeling costs and missing labels, the proposed method only requires a few labeled measurement data in addition to unlabeled data for training. Numerical simulations in three-phase unbalanced IEEE 13-bus and 123-bus distribution systems validate the detection accuracy and efficiency of this method.