  recent literature has proposed various detection and identification methods
for FDIAs, but few studies have focused on a solution that would prevent such
attacks from occurring. However, great strides have been made using deep
learning to detect attacks. Inspired by these advancements, we have developed a
new methodology for not only identifying AC FDIAs but, more importantly, for
correction as well. Our methodology utilizes a Long-Short Term Memory Denoising
Autoencoder (LSTM-DAE) to correct attacked-estimated states based on the
attacked measurements. The method was evaluated using the IEEE 30 system, and
the experiments demonstrated that the proposed method was successfully able to
identify the corrupted states and correct them with high accuracy.
