Enhancing Cyber-Resiliency of DER-based SmartGrid: A Survey

• URL: http://arxiv.org/abs/2305.05338v3
• Date: Wed, 6 Mar 2024 02:59:06 GMT
• Title: Enhancing Cyber-Resiliency of DER-based SmartGrid: A Survey
• Authors: Mengxiang Liu, Fei Teng, Zhenyong Zhang, Pudong Ge, Ruilong Deng, Mingyang Sun, Peng Cheng, Jiming Chen,
• Abstract summary: This survey aims to provide a systematical and comprehensive review regarding the cyber-resiliency enhancement (CRE) of DER-based smart grid. An integrated threat modeling method is tailored for the hierarchical DER-based smart grid with special emphasis on vulnerability identification and impact analysis. A CRE framework is subsequently proposed to incorporate the five key resiliency enablers.
• Score: 15.633226785669203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid development of information and communications technology has enabled the use of digital-controlled and software-driven distributed energy resources (DERs) to improve the flexibility and efficiency of power supply, and support grid operations. However, this evolution also exposes geographically-dispersed DERs to cyber threats, including hardware and software vulnerabilities, communication issues, and personnel errors, etc. Therefore, enhancing the cyber-resiliency of DER-based smart grid - the ability to survive successful cyber intrusions - is becoming increasingly vital and has garnered significant attention from both industry and academia. In this survey, we aim to provide a systematical and comprehensive review regarding the cyber-resiliency enhancement (CRE) of DER-based smart grid. Firstly, an integrated threat modeling method is tailored for the hierarchical DER-based smart grid with special emphasis on vulnerability identification and impact analysis. Then, the defense-in-depth strategies encompassing prevention, detection, mitigation, and recovery are comprehensively surveyed, systematically classified, and rigorously compared. A CRE framework is subsequently proposed to incorporate the five key resiliency enablers. Finally, challenges and future directions are discussed in details. The overall aim of this survey is to demonstrate the development trend of CRE methods and motivate further efforts to improve the cyber-resiliency of DER-based smart grid.

Related papers

• GAN-GRID: A Novel Generative Attack on Smart Grid Stability Prediction [53.2306792009435]
  We propose GAN-GRID a novel adversarial attack targeting the stability prediction system of a smart grid tailored to real-world constraints. Our findings reveal that an adversary armed solely with the stability model's output, devoid of data or model knowledge, can craft data classified as stable with an Attack Success Rate (ASR) of 0.99.
  arXiv  Detail & Related papers  (2024-05-20T14:43:46Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• 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)
• From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks [82.964958051535]
  Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making. By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society.
  arXiv  Detail & Related papers  (2023-10-27T02:58:11Z)
• Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks [63.246437631458356]
  Next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native. This article introduces a novel framework for building AI-native wireless networks; grounded in the emerging field of causal reasoning. We highlight several wireless networking challenges that can be addressed by causal discovery and representation.
  arXiv  Detail & Related papers  (2023-09-23T00:05:39Z)
• Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid [11.122588110362706]
  The rapid growth of distributed energy resources (DERs) poses significant cybersecurity and trust challenges to the grid controller. To enable a trustworthy smart grid controller, this work investigates a trustworthy artificial intelligence (AI) mechanism for proactive identification and explanation of the cyber risk caused by the control/status message of DERs.
  arXiv  Detail & Related papers  (2023-06-12T02:28:17Z)
• FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination [1.0621485365427565]
  This paper proposes a novel Federated Learning-based privacy-preserving and communication-efficient attack detection framework, known as FedDiSC. We put forward a representation learning-based Deep Auto-Encoder network to accurately detect power system and cybersecurity anomalies. To adapt our proposed framework to the timeliness of real-world cyberattack detection in SGs, we leverage the use of a gradient privacy-preserving quantization scheme known as DP-SIGNSGD.
  arXiv  Detail & Related papers  (2023-04-07T13:43:57Z)
• ThreatKG: An AI-Powered System for Automated Open-Source Cyber Threat Intelligence Gathering and Management [65.0114141380651]
  ThreatKG is an automated system for OSCTI gathering and management. It efficiently collects a large number of OSCTI reports from multiple sources. It uses specialized AI-based techniques to extract high-quality knowledge about various threat entities.
  arXiv  Detail & Related papers  (2022-12-20T16:13:59Z)
• Web-Based Platform for Evaluation of Resilient and Transactive Smart-Grids [0.0]
  Transactive Energy (TE) is an emerging approach for managing increasing DERs in the smart-grids through economic and control techniques. We present a comprehensive web-based platform for evaluating resilience of smart-grids against a variety of cyber- and physical-attacks.
  arXiv  Detail & Related papers  (2022-06-11T15:34:33Z)
• Artificial Intelligence-Based Smart Grid Vulnerabilities and Potential Solutions for Fake-Normal Attacks: A Short Review [0.0]
  Smart grid systems are critical to the power industry, however their sophisticated architectural design and operations expose them to a number of cybersecurity threats. Artificial Intelligence (AI)-based technologies are becoming increasingly popular for detecting cyber assaults in a variety of computer settings. The present AI systems are being exposed and vanquished because of the recent emergence of sophisticated adversarial systems such as Generative Adversarial Networks (GAN)
  arXiv  Detail & Related papers  (2022-02-14T21:41:36Z)
• A Taxonomy of Cyber Defence Strategies Against False Data Attacks in Smart Grid [3.88835600711547]
  Modern electric power grid, known as the Smart Grid, has fast transformed the isolated and centrally controlled power system to a fast and massively connected cyber-physical system. The synergy of a vast number of cyber-physical entities has allowed the Smart Grid to be much more effective and sustainable in meeting the growing global energy challenges. However, it has also brought with it a large number of vulnerabilities resulting in breaches of data integrity, confidentiality and availability.
  arXiv  Detail & Related papers  (2021-03-30T05:36:09Z)

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.