Detection of false data injection attacks on smart grids based on a-bitg approach

HIGHLIGHTS

• What: The authors propose an A-BiTG detection model for FDIAs in massive amounts of grid data. The batch sizes of all models in the experiment were 32, the training epoch was 70, and the Adam optimizer was used to regulate the learning rate with an initial learning rate of 0.01 and a maximum time step of 100. Through the simulation of IEEE 14-bus and IEEE 118-bus systems under FDIA attacks, the authors drew the following conclusion: the model shows an excellent detection performance in the face of FDIAs with different intensities, which is . . .
  

   

  

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