Smart grid operators can use genetic algorithm feature selection to identify which electrical measurements matter most for attack detection, reducing sensor requirements while maintaining 98%+ accuracy.
This paper detects cyber-physical attacks in smart grids by combining machine learning with genetic algorithm-based feature selection. Using real power system data, the authors show that tree-based models like Extra Trees can accurately distinguish between natural faults and malicious attacks, and that a small subset of 27 features (down from 112) is sufficient for reliable detection.