Defense Strategy against False Data Injection Attacks in Ship DC Microgrids

False Data Injection Attacks (FDIA) on ship Direct Current (DC) microgrids may result in the priority trip of a large load, a black-out, and serious accidents of ship collisions when maneuvering in the port. The key of the prevention of FDIA is the detection of and countermeasures to false data. In this paper, a defense strategy is developed to detect and mitigate against FDIA on ship DC microgrids. First, a DC bus voltage estimator is trained with an Artificial Neural Network (ANN) model. The error between the estimate value and the measure value is compared with a threshold generated from history data to detect the occurrence of FDIA. Combined with the correlation of artificial neural network inputs, bad data are identified and recovered. The method is tested under six cases with different network and physical disturbances in Matlab/Simulink. The results show that the method can identify and mitigate the FDIA effectively; the error of identifying FDIA by ANN is less than 0.5 V. Therefore, the deviation between the actual bus voltage and the reference voltage is less than 0.5 V.

Automated summary

This paper proposes a defense strategy to detect and mitigate false data injection attacks on ship direct current microgrids. By training an artificial neural network model and comparing the error between estimated and measured values with a threshold generated from history data, the method successfully identifies and mitigates false data injection attacks.