A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities

TL;DR

This survey addresses the security challenges of modern smart grids by presenting a three-layer architectural framework and a comprehensive taxonomy of cyber, cyber-physical, and coordinated attacks. It reviews a broad set of detection and mitigation techniques, including game theory, graph theory, blockchain, and machine learning across supervised, unsupervised, semi-supervised, ensemble, and reinforcement learning paradigms, while also examining the emerging roles of LLMs and adversarial ML. The authors compare existing surveys, argue for an integrated approach across layers, and provide actionable future directions to enhance resilience, scalability, and real-time defense in smart grids. The work highlights practical implications for security architecture design, threat intelligence, and adaptive defense in complex, evolving grid environments.

Abstract

In this study, we conduct a comprehensive review of smart grid security, exploring system architectures, attack methodologies, defense strategies, and future research opportunities. We provide an in-depth analysis of various attack vectors, focusing on new attack surfaces introduced by advanced components in smart grids. The review particularly includes an extensive analysis of coordinated attacks that incorporate multiple attack strategies and exploit vulnerabilities across various smart grid components to increase their adverse impact, demonstrating the complexity and potential severity of these threats. Following this, we examine innovative detection and mitigation strategies, including game theory, graph theory, blockchain, and machine learning, discussing their advancements in counteracting evolving threats and associated research challenges. In particular, our review covers a thorough examination of widely used machine learning-based mitigation strategies, analyzing their applications and research challenges spanning across supervised, unsupervised, semi-supervised, ensemble, and reinforcement learning. Further, we outline future research directions and explore new techniques and concerns. We first discuss the research opportunities for existing and emerging strategies, and then explore the potential role of new techniques, such as large language models (LLMs), and the emerging threat of adversarial machine learning in the future of smart grid security.

Figures (6)

• Figure 1: Structure of This Study.
• Figure 2: Layers in Smart Grid
• Figure 3: A workflow illustrating how blockchain technology improves smart grid security.
• Figure 4: Overview of Machine Learning-Based Attack Detection in Smart Grids.
• Figure 5: A representation of the Reinforcement Learning process in Smart Grids for attack detection.