A comprehensive review of blockchain and machine learning integration for cybersecurity in microgrids

The combination of blockchain technology with machine learning (ML) has emerged as a transformative approach to addressing cybersecurity challenges in microgrids (MG). As these systems become increasingly interconnected and dependent on real-time data transmission, they face growing risks from cyber threats such as false data injection attacks (FDIA), data tampering, denial of service (DoS), and adversarial attacks. This study provides a comprehensive analysis of how blockchain and ML can be integrated to mitigate these vulnerabilities. While ML offers advanced capabilities for anomaly detection, threat prediction, and adaptive response, blockchain’s decentralized, transparent, and secure architecture provides a reliable foundation for data and transaction processing. By combining these technologies, MGs can enhance operational efficiency, safeguard data integrity, and strengthen system resilience. This article reviews recent developments in blockchain and ML applications for MG cybersecurity and highlights key enabling technologies and implementation challenges. Future research directions include the design of hybrid models and improvements in scalability. The findings highlight the potential of blockchain and ML to transform cybersecurity in MGs and support the development of safer, more reliable, and sustainable energy systems.