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Security of Smart Grid: From Cryptography to Artificial Intelligence

This special issue belongs to the section “Security Engineering & Applications“.

Special Issue Information

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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Keywords

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

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J. Cybersecur. Priv. - ISSN 2624-800X

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