A Comprehensive Survey on the Cyber-Security of Smart Grids: Cyber-Attacks, Detection, Countermeasure Techniques, and Future Directions

• URL: http://arxiv.org/abs/2207.07738v1
• Date: Wed, 22 Jun 2022 14:55:06 GMT
• Title: A Comprehensive Survey on the Cyber-Security of Smart Grids: Cyber-Attacks, Detection, Countermeasure Techniques, and Future Directions
• Authors: Tala Talaei Khoei, Hadjar Ould Slimane, and Naima Kaabouch
• Abstract summary: We provide a classification of attacks based on the Open System Interconnection (OSI) model. We discuss in more detail the cyber-attacks that can target the different layers of smart grid networks communication.
• Score: 0.5735035463793008
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One of the significant challenges that smart grid networks face is cyber-security. Several studies have been conducted to highlight those security challenges. However, the majority of these surveys classify attacks based on the security requirements, confidentiality, integrity, and availability, without taking into consideration the accountability requirement. In addition, some of these surveys focused on the Transmission Control Protocol/Internet Protocol (TCP/IP) model, which does not differentiate between the application, session, and presentation and the data link and physical layers of the Open System Interconnection (OSI) model. In this survey paper, we provide a classification of attacks based on the OSI model and discuss in more detail the cyber-attacks that can target the different layers of smart grid networks communication. We also propose new classifications for the detection and countermeasure techniques and describe existing techniques under each category. Finally, we discuss challenges and future research directions.

Related papers

• A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes [1.3631461603291568]
  Generative Adversarial Networks (GANs) have emerged as powerful solutions for addressing the constantly changing security issues. This survey studies the significance of the deep learning model, precisely on GANs, in strengthening cybersecurity defenses. The focus is to examine how GANs can be influential tools to strengthen cybersecurity defenses in these domains.
  arXiv  Detail & Related papers  (2024-07-11T19:51:48Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey [1.0589208420411014]
  This study explores proactive cyber defense strategies utilizing Deep Learning (DL) in Smart Grids. A significant focus is placed on DL-enabled proactive defenses, highlighting their roles and relevance in the proactive security of SG. The survey lists the challenges associated with deploying DL-based security systems within SG, followed by an outlook on future developments in this key field.
  arXiv  Detail & Related papers  (2024-01-11T13:14:40Z)
• An Approach to Abstract Multi-stage Cyberattack Data Generation for ML-Based IDS in Smart Grids [2.5655761752240505]
  We propose a method to generate synthetic data using a graph-based approach for training machine learning models in smart grids. We use an abstract form of multi-stage cyberattacks defined via graph formulations and simulate the propagation behavior of attacks in the network.
  arXiv  Detail & Related papers  (2023-12-21T11:07:51Z)
• Graph Mining for Cybersecurity: A Survey [61.505995908021525]
  The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
  arXiv  Detail & Related papers  (2023-04-02T08:43:03Z)
• 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)
• False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey [1.9084046244608193]
  The integration of several cutting-edge technologies has introduced several security and privacy vulnerabilities. Recent research trends have shown that False Data Injection (FDI) attacks are becoming one of the most malicious cyber threats within the entire smart grid paradigm.
  arXiv  Detail & Related papers  (2021-11-28T22:25:15Z)
• Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety [54.478842696269304]
  The use of deep neural networks (DNNs) in safety-critical applications is challenging due to numerous model-inherent shortcomings. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. Our paper addresses both machine learning experts and safety engineers.
  arXiv  Detail & Related papers  (2021-04-29T09:54:54Z)
• 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)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)
• Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure [0.0]
  The smart grid is exposed to a wide variety of threats that could be translated into cyber-attacks. In this paper, we develop a deep learning-based intrusion detection system to defend against cyber-attacks.
  arXiv  Detail & Related papers  (2019-12-31T21:06:20Z)

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.