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

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.