Cyber-Physical Anomaly Detection in IoT-Enabled Smart Grids Using Machine Learning and Metaheuristic Feature Optimization
Researchers have developed a machine learning approach to detect cyber-physical anomalies in smart grids, aiming to distinguish between physical faults and malicious cyber-attacks. The method utilizes genetic algorithms for feature selection, reducing the number of required measurements while improving detection accuracy. Tree-based ensemble models, particularly Extra Trees, demonstrated the highest effectiveness, achieving an increased macro-F1 score and ROC-AUC with a significantly reduced feature set. AI
IMPACT This research could lead to more robust and efficient anomaly detection systems for smart grids, improving their resilience against cyber-physical threats.
