Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models - Aziz, Saddam; Irshad, Muhammad; Haider, Sami Ahmed; Wu, Jianbin; Deng, Ding Nan; Ahmad, Sadiq (Frontiers in Energy Research, 2022)
False data injection (FDI) attacks regularly target smart grids. It is impossible to detect FDI attacks using current bad data detection methods. Machine learning is one method for detecting FDI attacks. Each of six supervised learning (SVM-FS) hybrid strategies is examined using six distinct boosting and feature selection (FS) methodologies. A smart grid dataset is used to evaluate the applicability of different technologies. Detection methods are compared based on how accurate they are in detecting specific threats. When supervised learning and hybrid methods are applied in a simulation exercise, classification algorithms used to detect FDI attacks perform better.