False Data Injection Attack Detection Method Based onImproved Generative Adversarial Network

doi:10.12204/j.issn.1000-7229.2022.03.007

Abstract

Abstract:

With the development of new-type energy internet, large-scale sensing measurement systems provide data support for data-driven detection of false data injection attack. However, the problem of unbalanced attack data will affect the performance of such methods. Therefore, a data rebalance attack detection model based on improved generative adversarial network (GAN) and extremely randomized tree is proposed. Firstly, the GAN structure is designed to make the training procedure stable enough to generate high-quality data. Secondly, the Copula function is used to construct the spatial correlation between the power system states to adapt to the integration of the distributed energy resources. Then, a rebalanced dataset is obtained through the adversarial training of the improved GAN, and the extremely randomized tree classifier is used to detect the attack. In addition, the data validity index based on multiple classifiers is designed to evaluate the quality of the generated data. The effect of the proposed method is verified by comparative experiments. Results show that the method can generate high-quality measurement data, solve the problem of data imbalance, and the attack detection rate is 98.95%.

Key words: false data injection attack, generative adversarial network, extremely randomized tree, imbalanced dataset, machine learning, attack detection

CLC Number:

TM76

XIA Yunshu, WANG Yong, ZHOU Lin, FAN Rusen. False Data Injection Attack Detection Method Based onImproved Generative Adversarial Network[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(3): 58-65.

Figures/Tables 9

Fig.1 Structure of FDIA detection based onCopulaGAN method

Table 1 Confusion matrix for binary classification

Table 2 Description of the training set

Table 3 Hyperparameters of the model

Table 4 Similarity score of synthetic data

Fig.2 Effectiveness evaluation of synthetic data

Table 5 Accuracy of algorithms on original dataset

Fig.3 Confusion matrix of FDIA detection

Table 6 Performance of ET algorithm withdiffrent oversampling methods

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