Identification and Correction of False Data Injection Attacks against AC State Estimation using Deep Learning
This addresses security vulnerabilities in power grid systems, offering a correction solution beyond detection, though it is incremental as it builds on existing deep learning approaches.
The paper tackled the problem of false data injection attacks in AC state estimation by developing a deep learning method that identifies and corrects such attacks, achieving high accuracy in experiments on the IEEE 30 system.
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.