Two-stage Detection Method for DC Microgrid False Data Injection Attack Based on Deep Learning

doi:10.11930/j.issn.1004-9649.202311112

Abstract

Abstract:

A direct current (DC) microgrid is a cyber-physical information system that is susceptible to network attacks in the process of information transmission. Attackers can impact the security of the microgrid system by injecting false data into the information channel. Detecting and correcting false data injection attacks can enhance the security of the microgrid system operation. To address this issue, a two-stage false data injection attack detection method is proposed based on convolutional neural network (CNN) and long short-term memory (LSTM) combined with maximum information coefficient (MIC). Firstly, the CNN is used to extract the temporal features from the time series data of the DC microgrid operation. And the LSTM model, combined with the temporal features extracted by CNN, is then used to predict the operating state of the DC microgrid. This predicted value is compared with the actual value to preliminarily determine the presence of false data in the system. Secondly, considering the potential false positive rate of the CNN-LSTM model, an MIC verifier is constructed to further determine the presence of false data in the system and restore the data. The rationality and feasibility of the proposed method were verified through Matlab simulation analysis of the DC microgrid.

Key words: DC microgrid, false data injection attack, long short-term memory network, maximum mutual information coefficient

Lei TAO, Pingping LUO, Jikeng LIN. Two-stage Detection Method for DC Microgrid False Data Injection Attack Based on Deep Learning[J]. Electric Power, 2024, 57(9): 11-19.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202311112

https://www.electricpower.com.cn/EN/Y2024/V57/I9/11

Figures/Tables 11

Fig.1 Control model of DC microgrid

Fig.2 Input data structure of CNN-LSTM network model

Fig.3 Flow chart of CNN-LSTM detection model

Fig.4 Two-stage FDIA detection mechanism based on CNN-LSTM combined with MIC

Fig.5 False data correction model

Fig.6 The current and voltage value of DC microgrid with load added

Fig.7 The voltage and current value of DC microgrid after adding false data

Table 1 The MIC value before and after the attack

DG	正常运行	负荷变化	FDIA
1	0.92	0.91	0.078
2	0.88	0.88	0.065
3	0.84	0.85	0.080
4	0.89	0.88	0.068

Fig.8 The voltage and current value of DC microgrid

Fig.9 Line current and bus voltage errors before and after the attack

Table 2 Test results of different models

检测模型	A/%	P/%	R/%	F/%
BP检测模型	91.62	91.54	91.71	91.63
LSTM检测模型	92.67	93.37	91.87	92.61
二阶段检测模型	94.04	95.29	92.65	93.95

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