Comprehensive Review of Challenges and Advances in Intrusion Detection for Smart Grid Systems

Smart grids integrate advanced technologies to improve the efficiency, reliability, and sustainability of power distribution, but they also introduce significant cybersecurity vulnerabilities. This manuscript provides a comprehensive review of the challenges and advances in intrusion detection systems (IDS) for smart grids. Key challenges include handling large volumes of heterogeneous data, ensuring real-time detection with low false positives, and addressing the evolving sophistication of cyber-attacks. Recent developments in artificial intelligence (AI), machine learning (ML), and blockchain technology offer promising solutions to these challenges, enhancing the ability of IDS to detect, prevent, and mitigate cyber-attacks. This review explores AI-based models like deep learning and reinforcement learning, alongside blockchain’s role in securing smart grid transactions. By examining the current state of IDS research and identifying areas for improvement, this paper contributes to the ongoing efforts to strengthen smart grid cybersecurity and ensure grid stability in the face of growing cyber threats.