Power system state estimation (PSSE) plays a vital role in stable operation of modern smart grids, while it is vulnerable to cyber attacks. False data injection attacks (FDIA), one of the most common cyber attacks, can tamper with measurement data and bypass the bad data detection (BDD) mechanism, leading to incorrect PSSE results.

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[1] Bairen Chen, Kaishun Xiahou, "Data Recovery for False Data Injection Attacks of Power Systems Based on Lightweight Variational Convolution Auto-Encoder", IEEE Dataport, 2022. [Online]. Available: http://dx.doi.org/10.21227/14h3-er18. Accessed: Feb. 04, 2023.
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doi = {10.21227/14h3-er18},
url = {http://dx.doi.org/10.21227/14h3-er18},
author = {Bairen Chen; Kaishun Xiahou },
publisher = {IEEE Dataport},
title = {Data Recovery for False Data Injection Attacks of Power Systems Based on Lightweight Variational Convolution Auto-Encoder},
year = {2022} }
TY - DATA
T1 - Data Recovery for False Data Injection Attacks of Power Systems Based on Lightweight Variational Convolution Auto-Encoder
AU - Bairen Chen; Kaishun Xiahou
PY - 2022
PB - IEEE Dataport
UR - 10.21227/14h3-er18
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Bairen Chen, Kaishun Xiahou. (2022). Data Recovery for False Data Injection Attacks of Power Systems Based on Lightweight Variational Convolution Auto-Encoder. IEEE Dataport. http://dx.doi.org/10.21227/14h3-er18
Bairen Chen, Kaishun Xiahou, 2022. Data Recovery for False Data Injection Attacks of Power Systems Based on Lightweight Variational Convolution Auto-Encoder. Available at: http://dx.doi.org/10.21227/14h3-er18.
Bairen Chen, Kaishun Xiahou. (2022). "Data Recovery for False Data Injection Attacks of Power Systems Based on Lightweight Variational Convolution Auto-Encoder." Web.
1. Bairen Chen, Kaishun Xiahou. Data Recovery for False Data Injection Attacks of Power Systems Based on Lightweight Variational Convolution Auto-Encoder [Internet]. IEEE Dataport; 2022. Available from : http://dx.doi.org/10.21227/14h3-er18
Bairen Chen, Kaishun Xiahou. "Data Recovery for False Data Injection Attacks of Power Systems Based on Lightweight Variational Convolution Auto-Encoder." doi: 10.21227/14h3-er18