Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models

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 can be used machine learning. The purpose of this study analyse each six supervised learning (SVM-FS) hybrid techniques using different boosting and feature selection (FS) methodologies. A dataset from grid utilised in process determining applicability various technologies. Comparisons detection strategies made based on how accurately one kinds threats. performance classification algorithms detect assaults improved by application methods a simulated exercise.