AUTHOR=Aziz Saddam , Irshad Muhammad , Haider Sami Ahmed , Wu Jianbin , Deng Ding Nan , Ahmad Sadiq TITLE=Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models JOURNAL=Frontiers in Energy Research VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.964305 DOI=10.3389/fenrg.2022.964305 ISSN=2296-598X ABSTRACT=

False data injection (FDI) attacks commonly target smart grids. Using the tools that are now available for detecting incorrect data, it is not possible to identify FDI attacks. One way that can be used to identify FDI attacks is machine learning. The purpose of this study is to analyse each of the six supervised learning (SVM-FS) hybrid techniques using the six different boosting and feature selection (FS) methodologies. A dataset from the smart grid is utilised in the process of determining the applicability of various technologies. Comparisons of detection strategies are made based on how accurately each one can identify different kinds of threats. The performance of classification algorithms that are used to detect FDI assaults is improved by the application of supervised learning and hybrid methods in a simulated exercise.