A Deep Learning Framework to Identify Remedial Action Schemes Against False Data Injection Cyberattacks Targeting Smart Power Systems
Ehsan Naderi, Arash Asrari
Abstract
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 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">link</i> between transmission/distribution sectors to react to the cyberattacks for which similar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">preinvestigated</i> 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 preinvestigated by system operator.