Secure Smart Energy Systems Anomaly Detection: Advanced Methods with AI, Moving Target Defense and Quantum Computing

Submission Deadline: 31 January 2027 View: 10 Submit to Special Issue

Guest Editors

Prof. Dr. Zhenyong Zhang

Email: zhangzy@gzu.edu.cn

Affiliation: Department of Information Security, Guizhou University, Guiyang, China

Homepage:

Research Interests: cyber-physical system security, industrial control system security, machine learning security, metaverse security, big data analysis

Prof. Dr. Mengxiang Liu

Email: mengxiang.liu@bristol.ac.uk

Affiliation: School of Computer Science, University of Bristol, Bristol, United Kingdom

Homepage:

Research Interests: smart grid, cyber-physical system security, industrial control system security

Summary

The modernization of power grids into smart grids has greatly enhanced efficiency and reliability but also expanded the attack surface for cyber‑physical threats. Anomaly detection is a cornerstone of smart grid security; however, traditional methods struggle against sophisticated, stealthy attacks and the high‑dimensional, time‑varying nature of grid data.

This Special Issue focuses on advanced anomaly detection paradigms that go beyond conventional machine learning. We invite contributions on: (1) AI‑based methods – deep learning, federated learning, explainable AI for real‑time anomaly identification; (2) Moving Target Defense (MTD) – proactive, dynamic defense mechanisms that reconfigure system parameters to invalidate attacker knowledge; and (3) Quantum Computing – quantum machine learning, quantum anomaly detection algorithms, and hybrid quantum‑classical approaches for scalable and ultra‑fast detection.

The aim is to gather cutting‑edge research that bridges theoretical advances with practical deployment in smart grids. We welcome original research articles, reviews, and case studies.

Suggested themes include (but are not limited to):
(1) Deep learning and graph neural networks for anomaly detection in PMU/SCADA data
(2) Federated learning for privacy‑preserving grid monitoring
(3) Moving target defense strategies for smart grid communication and control
(4) Quantum‑enhanced anomaly detection using quantum support vector machines or quantum kernels
(5) Hybrid AI‑MTD frameworks for cyber‑physical resilience
(6) Real‑world testbed implementations and benchmark datasets
(7) Scalability and latency challenges for quantum‑inspired algorithms

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