Defense Method for Smart Grid GPS Spoofing Attack Based on BiLSTM and Self-attention Mechanism Generative Adversarial Network

doi:10.11930/j.issn.1004-9649.202311025

Abstract

Abstract:

Phasor measurement unit (PMU) plays a crucial role in smart grids, enabling precise synchronized acquisition of electric power data. Due to the use of the global positioning system (GPS) for time synchronization, the PMU is vulnerable to GPS spoofing attack (GSA), which impacts the normal data acquisition. The existing GSA defense methods have low restoration accuracy and require additional hardware costs. To address the aforementioned issues, this paper proposes a GSA defense method based on bidirectional long short-term memory (BiLSTM) network and self-attention mechanism generative adversarial network. Firstly, an improved WGAN-GP model is proposed to redesign the network architecture of the generator and discriminator, and the BiLSTM network and self-attention mechanism are incorporated into the generator and discriminator to enhance the model's generative performance and discriminative ability. Secondly, based on the proposed WGAN-GP model, a GSA defense model is constructed, which includes two crucial modules: an attack detection network and a data restoration network that are employed to detect the smart grid GSA and repair the compromised PMU measurement data, respectively. Finally, We simulated GSA attacks in the IEEE-39 bus system and validated the effectiveness of the proposed method on the corresponding dataset. The results show that compared to existing methods, the proposed approach outperforms in most performance indicators.

Key words: PMU, attack detection, data repair, GSA, WGAN-GP model

Hui WU, Ziwei ZOU, Fengming XIAO, Jie LIU, Chenpeng MIN, Zhuoqun XIA. Defense Method for Smart Grid GPS Spoofing Attack Based on BiLSTM and Self-attention Mechanism Generative Adversarial Network[J]. Electric Power, 2024, 57(9): 61-70.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202311025

https://www.electricpower.com.cn/EN/Y2024/V57/I9/61

Figures/Tables 13

Fig.1 Schematic of mutant GSA

Fig.2 Schematic of slow sustained GSA

Fig.3 GSA defense model

Fig.4 Training workflow of data repair network

Fig.5 Structure of the generator

Fig.6 Architecture of the discriminator

Fig.7 IEEE-39 bus system

Table 1 Datasets used in this paper

类别	PMU个数	数据量
正常PMU数据	29	8759
遭受突变型GSA的PMU数据	4	1276
遭受慢速持续型GSA的PMU数据	6	1867

Table 2 Ablation experiments

方法	准确率/%	精确率/%	召回率/%	F1 分数/%
WGAN-GP	87.23	82.06	80.36	81.56
WGAN-GP+自注意力层	91.64	84.07	81.97	83.64
WGAN-GP+BiLSTM	92.66	84.96	82.39	83.97
本文方法	95.24	86.15	81.63	85.07

Table 3 Comparison of detection model metrics among different algorithms

算法	准确率/%	AUC/%	训练时间/s	检测时间/s
文献[16]	94.32	94.57	43.24	0.42
文献[18]	92.38	90.81	39.29	0.32
文献[17]	91.32	91.30	46.08	0.30
本文方法	95.24	95.58	47.98	0.50

Fig.8 Comparison of repair similarity of different algorithms in single-type GSA scenarios

Table 4 The accuracy of repaired data in different types of GSA scenarios 单位： %

场景类型	本文方法	WGAN-GP模型	文献[19]	文献[22]
单类型GSA	94.44	92.38	85.68	93.74
多类型GSA	92.63	89.72	—	91.77

Fig.9 Comparison of repair similarities of different algorithms in multi-type GSA scenarios

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