A Deep Learning Framework to Identify Remedial Action Schemes Against False Data Injection Cyberattacks Targeting Smart Power Systems

This article proposes a remedial action scheme (RAS) based on the concept of deep learning to mitigate the impacts of false data injection (FDI) cyberattacks on smart power systems. As a prerequisite of such a RAS, power system operator is being in attacker's shoe to scrutinize different scenarios of cyberattacks. In design of the RAS, long short-term memory (LSTM) cells have been integrated into a deep recurrent neural network (DRNN) to effectively process the data of an intelligent archive framework (IAF), identifying the proper reaction mechanisms. Power flow analysis has been considered to examine the interdependence between transmission/distribution sectors to react to the cyberattacks for which similar pre-investigated remedial actions have not been saved in the IAF. Effectiveness of the proposed RAS is validated on two IEEE transmission/distribution systems, where consequences of FDI cyberattacks are reduced by 30% in case of experiencing attacks, which are not pre-investigated by system operator.