mlsanityData to 14 Oct 2026

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

arXiv 2407.07966 ↗First posted 10 Jul 2024cs.CR cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 69 of its 378 references that are in the map. First posted on arXiv 10 Jul 2024.

Builds on

69 of 378 references.

References · 69

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38 more
  1. 2017Evasion Attacks against Machine Learning at Test Timecited by 689
  2. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 678
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50 most cited of 69 All 69 (JSON)

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Citing papers per year: 2025: 4.

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  3. 2025Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and Benchmarkcited by 5
  4. 2025Real-Time Bus Departure Prediction Using Neural Networks for Smart IoT Public Bus Transitcited by 0

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