This review explores the evaluation of feature selection techniques using machine learning for detecting cyber-attacks in smart grids. As smart grids integrate advanced technologies to enhance power distribution and management, they become more susceptible to cyber threats. Effective feature selection is crucial to improve the performance of machine learning models in identifying these threats. This study analyzes various feature selection methods, including filter, wrapper, and embedded approaches, and their impact on model accuracy, precision, and recall. By reviewing recent research, we highlight the strengths and limitations of different techniques and suggest best practices for their application in smart grid cybersecurity. Our findings aim to guide future research and development in enhancing the resilience of smart grids against cyber-attacks.

Keywords: Feature selection, machine learning, cyber-attack detection, smart grid,cybersecurity, filter methods, wrapper methods, embedded methods, model performance, resilience.