The field of energy management systems is moving towards developing innovative solutions to enhance cyber resilience in the face of escalating cyber threats. Researchers are exploring the integration of federated learning, anomaly detection, and mitigation techniques to maintain operational reliability and economic efficiency in microgrid energy management systems under cyberattack conditions. Notable advancements include the development of comprehensive cyber-resilient frameworks, novel two-stage cascade false data injection attack detection, and energy management system optimization. These approaches combine autoencoder reconstruction error with prediction uncertainty quantification to enable attack-resilient energy storage scheduling while preserving data privacy. Furthermore, the design and evaluation of virtualized Intelligent Electronic Devices (IEDs) for digital substations have shown promising results, offering a cost-effective solution with scalability, simplified maintenance, and reduced hardware costs. The incorporation of AI-enabled hybrid cyber-physical frameworks for adaptive control in smart grids is also a significant development, leveraging machine learning-based digital forensic frameworks to detect, identify, and mitigate security incidents in real-time.

Noteworthy papers include: Uncertainty-Aware Federated Learning for Cyber-Resilient Microgrid Energy Management, which proposes a comprehensive cyber-resilient framework that reduced false positive detections by 70% and achieved 5% operational cost savings. Federated Anomaly Detection and Mitigation for EV Charging Forecasting Under Cyberattacks, which presents a novel anomaly-resilient federated learning framework that simultaneously preserves data privacy and detects cyber-attacks, achieving a 15.2% improvement in R2 accuracy. An AI-Enabled Hybrid Cyber-Physical Framework for Adaptive Control in Smart Grids, which introduces a machine learning-based digital forensic framework that combines data acquisition, authenticated communication, scalable cloud storage, and automated forensic analytics to detect and mitigate security incidents in real-time.