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Identification and Correction of False Data Injection Attacks against AC State Estimation using Deep Learning

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Document pages: 5 pages

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

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