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

• URL: http://arxiv.org/abs/2407.07966v1
• Date: Wed, 10 Jul 2024 18:03:24 GMT
• Title: A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities
• Authors: Arastoo Zibaeirad, Farnoosh Koleini, Shengping Bi, Tao Hou, Tao Wang,
• Abstract summary: We provide an in-depth analysis of various attack vectors, focusing on new attack surfaces introduced by advanced components in smart grids. Following this, we examine innovative detection and mitigation strategies, including game theory, graph theory, and machine learning. We first discuss the research opportunities for existing and emerging strategies, and then explore the potential role of new techniques.
• Score: 4.589028594967462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
  generative AI, particularly large language models (LLMs), become increasingly integrated into production applications. New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems. Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks. This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
  arXiv  Detail & Related papers  (2024-09-23T10:18:10Z)
• Recent Advances in Attack and Defense Approaches of Large Language Models [27.271665614205034]
  Large Language Models (LLMs) have revolutionized artificial intelligence and machine learning through their advanced text processing and generating capabilities. Their widespread deployment has raised significant safety and reliability concerns. This paper reviews current research on LLM vulnerabilities and threats, and evaluates the effectiveness of contemporary defense mechanisms.
  arXiv  Detail & Related papers  (2024-09-05T06:31:37Z)
• Reinforcement Learning-Based Approaches for Enhancing Security and Resilience in Smart Control: A Survey on Attack and Defense Methods [0.3626013617212667]
  Reinforcement Learning (RL) learns to make decisions based on real-world experiences. This paper reviews the latest adversarial RL threats and outlines effective defense strategies tailored to safeguard these applications. By concentrating on the smart grid and smart home scenarios, this survey equips ML developers and researchers with the insights needed to secure RL applications.
  arXiv  Detail & Related papers  (2024-02-23T21:48:50Z)
• Designing an attack-defense game: how to increase robustness of financial transaction models via a competition [69.08339915577206]
  Given the escalating risks of malicious attacks in the finance sector, understanding adversarial strategies and robust defense mechanisms for machine learning models is critical. We aim to investigate the current state and dynamics of adversarial attacks and defenses for neural network models that use sequential financial data as the input. We have designed a competition that allows realistic and detailed investigation of problems in modern financial transaction data. The participants compete directly against each other, so possible attacks and defenses are examined in close-to-real-life conditions.
  arXiv  Detail & Related papers  (2023-08-22T12:53:09Z)
• Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook [2.1771693754641013]
  We aim to provide a thorough understanding of the concept of physical adversarial attacks, analyzing their key characteristics and distinguishing features. Our article delves into various physical adversarial attack methods, categorized according to their target tasks in different applications. We assess the performance of these attack methods in terms of their effectiveness, stealthiness, and robustness.
  arXiv  Detail & Related papers  (2023-08-11T15:02:19Z)
• On the Security Risks of Knowledge Graph Reasoning [71.64027889145261]
  We systematize the security threats to KGR according to the adversary's objectives, knowledge, and attack vectors. We present ROAR, a new class of attacks that instantiate a variety of such threats. We explore potential countermeasures against ROAR, including filtering of potentially poisoning knowledge and training with adversarially augmented queries.
  arXiv  Detail & Related papers  (2023-05-03T18:47:42Z)
• Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey [114.17568992164303]
  Adrial attacks and defenses in machine learning and deep neural network have been gaining significant attention. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks.
  arXiv  Detail & Related papers  (2023-03-11T04:19:31Z)
• Holistic Adversarial Robustness of Deep Learning Models [91.34155889052786]
  Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability. This paper provides a comprehensive overview of research topics and foundational principles of research methods for adversarial robustness of deep learning models.
  arXiv  Detail & Related papers  (2022-02-15T05:30:27Z)
• Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain [58.30296637276011]
  This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques. It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
  arXiv  Detail & Related papers  (2020-07-05T18:22:40Z)
• Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities [22.976960488191505]
  Advanced deep learning techniques provide a new paradigm to learn end-to-end models from complex data. The existing studies show that compared with traditional machine learning algorithms, deep learning models can improve the performance of insider threat detection. Applying deep learning to further advance the insider threat detection task still faces several limitations, such as lack of labeled data, adaptive attacks.
  arXiv  Detail & Related papers  (2020-05-25T22:48:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.