AI-driven cybersecurity-based hybrid approach using blockchain for smart grids

Abstract

Power systems have become more efficient, reliable, and sustainable as smart grids are increasingly integrated into them. Developing innovative security solutions is often required to counter evolving cyber threats because traditional security mechanisms lack real-time protection. The purpose of this paper is to propose a blockchain-based hybrid optimization approach for enhancing smart grid security and resilience. To improve decision-making, resource allocation, and attack mitigation, the framework incorporates artificial intelligence (AI) to detect anomalies in real-time, blockchain technology to store unrestricted data, and a hybrid optimization algorithm to make decisions in real-time. Adaptive Vulture Optimization Algorithm (AVOA) and Convolutional Neural Networks (CNN) combine to reduce computational overhead and maintain detection accuracy effectively. According to the proposed approach, which is compared to existing models, including ML-ID, HHT, and THD, it achieves superior performance, with a detection rate of 99.17%, a reduction in computation time, and enhanced scalability. These results demonstrate that AI-Blockchain hybrid frameworks are effective in protecting smart grids against emerging cyber threats, making them a reliable and scalable security solution.