Digital Twin for Evaluating Detective Countermeasures in Smart Grid Cybersecurity
- URL: http://arxiv.org/abs/2412.03973v1
- Date: Thu, 05 Dec 2024 08:41:08 GMT
- Title: Digital Twin for Evaluating Detective Countermeasures in Smart Grid Cybersecurity
- Authors: Omer Sen, Nathalie Bleser, Andreas Ulbig,
- Abstract summary: 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.
- Score: 0.0
- License:
- Abstract: As the integration of digital technologies and communication systems continues within distribution grids, new avenues emerge to tackle energy transition challenges. Nevertheless, this deeper technological immersion amplifies the necessity for resilience against threats, encompassing both systemic outages and targeted cyberattacks. To ensure the robustness and safeguarding of vital infrastructure, a thorough examination of potential smart grid vulnerabilities and subsequent countermeasure development is essential. This study delves into the potential of digital twins, replicating a smart grid's cyber-physical laboratory environment, thereby enabling focused cybersecurity assessments. Merging the nuances of communication network emulation and power network simulation, we introduce a flexible, comprehensive digital twin model equipped for hardware-in-the-loop evaluations. Through this innovative framework, we not only verify and refine security countermeasures but also underscore their role in maintaining grid stability and trustworthiness.
Related papers
- Machine Learning-Based Intrusion Detection and Prevention System for IIoT Smart Metering Networks: Challenges and Solutions [0.0]
This paper explores the challenges associated with securing IIoT-based smart metering networks.
It proposes a Machine Learning-based Intrusion Detection and Prevention System (IDPS) for safeguarding edge devices.
arXiv Detail & Related papers (2025-02-16T14:08:59Z) - 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) - Simulation of Multi-Stage Attack and Defense Mechanisms in Smart Grids [2.0766068042442174]
We introduce a simulation environment that replicates the power grid's infrastructure and communication dynamics.
The framework generates diverse, realistic attack data to train machine learning algorithms for detecting and mitigating cyber threats.
It also provides a controlled, flexible platform to evaluate emerging security technologies, including advanced decision support systems.
arXiv Detail & Related papers (2024-12-09T07:07:17Z) - A cyber-physical digital twin approach to replicating realistic multi-stage cyberattacks on smart grids [2.479074862022315]
This paper examines the impact of cyberattacks on smart grids by replicating the power grid in a secure laboratory environment.
A simulation is used to study communication infrastructures for secure operation of smart grids.
arXiv Detail & Related papers (2024-12-06T09:58:51Z) - Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
We propose Authenticated Cyclic Redundancy Integrity Check (ACRIC)
ACRIC preserves backward compatibility without requiring additional hardware and is protocol agnostic.
We show that ACRIC offers robust security with minimal transmission overhead ( 1 ms)
arXiv Detail & Related papers (2024-11-21T18:26:05Z) - Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI [0.0]
A key area in AI-based cybersecurity focuses on defending deep neural networks against malicious perturbations.
We attempt to validate results from prior work on certified robustness using the VeriGauge toolkit.
Our findings underscore the urgent need for standardized methodologies, containerization, and comprehensive documentation.
arXiv Detail & Related papers (2024-05-29T04:37:19Z) - 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) - 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) - 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) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z)
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.