Security of Smart Grid: From Cryptography to Artificial Intelligence

Dear Colleagues,

The reliable and secure operation of smart grids depends on authentication and secure communication between nodes at all hierarchical levels of the smart grid, that is, between the central control station (SCADA) and substations, between the distributed energy resource (DER) controller and SCADA, between phasor measurement units (PMUs) and phasor data concentrators (PDCs), between DERs and the substation, and between smart meters and the utility provider. The existing standards for data communication between smart grid nodes either do not specify cybersecurity specifications for inter-node communication or permit security vulnerabilities. This Special Issue targets novel approaches for authentication, symmetric key establishment, and encryption between smart grid nodes at all hierarchical levels. Furthermore, this Special Issue targets artificial intelligence (AI)-based approaches for attack detection and mitigation at all hierarchical levels of smart grids, including the DER primary controller. These AI-based approaches aim to minimize the impact of a potential security breach and trigger post-attack control schemes for resiliency enhancement.

Dr. Arslan Munir
Guest Editor

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• cryptography for smart grids
• post-quantum cryptography for smart grids
• AI for smart grid security
• deep learning for smart grid security
• distributed energy resource (DER) security
• transformer security
• large language models (LLMs) for smart grid security
• intrusion detection and anomaly detection in smart grid networks
• secure communication protocols for smart grids (SCADA, IEC 61850, DNP3, etc.)
• blockchain and distributed ledger technologies for smart grid security
• zero-trust architectures for smart grid infrastructures
• lightweight cryptography for constrained smart grid devices
• hybrid classical–post-quantum cryptographic protocols in grid communications
• hardware security modules (HSMs) and secure key management in smart grid environments
• federated learning for smart grid security and privacy preservation
• adversarial machine learning and robustness in smart grid AI models
• reinforcement learning for attack detection and response in grid operations
• graph neural networks (GNNs) for power grid anomaly detection
• security of demand response management and advanced metering infrastructure (AMI)
• edge and fog computing security for distributed smart grid applications
• cloud and data center security in smart grid ecosystems
• cyber–physical attack detection, modeling, and mitigation in smart grid systems
• quantum machine learning for smart grid intrusion detection and forecasting
• digital twins and simulation frameworks for testing smart grid cyber resilience
• privacy-preserving data analytics for energy consumption data
• regulatory and compliance frameworks for smart grid cybersecurity
• NIST/ISO/IEC standardization and adoption for smart grid systems
• risk assessment, threat modeling, and incident response in smart grids
• economic and societal impacts of cyber attacks on smart grids