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
- Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges [53.2306792009435]
We introduce a novel framework to detect instability in smart grids by employing only stable data.
It relies on a Generative Adversarial Network (GAN) where the generator is trained to create instability data that are used along with stable data to train the discriminator.
Our solution, tested on a dataset composed of real-world stable and unstable samples, achieve accuracy up to 97.5% in predicting grid stability and up to 98.9% in detecting adversarial attacks.
arXiv Detail & Related papers (2025-01-27T20:48:25Z) - Towards a Comprehensive Framework for Cyber-Incident Response Decision Support in Smart Grids [0.4077787659104315]
This paper presents a framework based on integrating Attack-Defense Trees and the Multi-Criteria Decision Making method to enhance smart grid cybersecurity.
The proposed model aims to optimize the effectiveness and efficiency of grid cybersecurity efforts while offering insights into future grid management challenges.
arXiv Detail & Related papers (2024-12-09T07:07:10Z) - Digital Twin for Evaluating Detective Countermeasures in Smart Grid Cybersecurity [0.0]
This study delves into the potential of digital twins, replicating a smart grid's cyber-physical laboratory environment.
We introduce a flexible, comprehensive digital twin model equipped for hardware-in-the-loop evaluations.
arXiv Detail & Related papers (2024-12-05T08:41:08Z) - 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) - 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) - 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) - 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.