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The Future of Cyber Security in Smart Grids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 1338

Special Issue Editors

School of Computer Science, University of Birmingham, University Rd W, Birmingham B15 2TT, UK
Interests: cyber security; smart grids; energy system digitalisation; communication networks
Department of Computer Science, The University of Manchester, Manchester ME13 9PL, UK
Interests: applied cryptography; information security; data privacy; smart grid; P2P energy trading
Department of Computer Science, University of York, Deramore Lane, York YO10 5GH, UK
Interests: cyber security; cryptography; provable security; protocol design and model

Special Issue Information

Dear Colleagues,

Smart Grids are smart electrical networks that use digital technologies to service the power grid by enabling fine-grain data collection, management, and analytics. Digital technologies can support the decarbonization and decentralization of Smart Grids, and provide communication, interconnection, and interdependency between various critical infrastructures, increase grid visibility, enable proactive maintenance, and manage demand and supply. However, these digital technologies can introduce complex cybersecurity challenges in Smart Grids. For example, home appliances and products that were traditionally offline, are becoming online, so we need to protect these devices, applications, and data against cyber-attack and data privacy threats. These cyber-attacks have negative impacts of varying severity on Smart Grids, such as causing cascading failure, affecting the capability of the energy system to deliver a reliable service, manipulating the market, and misguiding the billing systems.

This Special Issue aims to present and disseminate the latest understanding and development on cyber security challenges in future smart grids and to demonstrate solutions to mitigate the potential cyber threats and data privacy risks that smart technologies can introduce. We strongly encourage interdisciplinary work to be submitted.

Topics of particular interest include but are not limited to cyber security in smart grids, data privacy in smart grids, cyber-physical systems, cyber mitigation techniques for energy systems, EV charging stations, smart metering, communication protocols, communication networks, peer-to-peer energy trading, transactive energy and community/collective self-consumption.

Dr. Zoya Pourmirza
Dr. Mustafa A. Mustafa
Dr. Roberto Metere
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cyber security in smart grids
  • data privacy in smart grids
  • cyber physical systems
  • EV charging stations
  • smart metering
  • communication protocols
  • communication networks
  • peer-to-peer energy trading
  • transactive energy
  • community/collective self-consumption

Published Papers (1 paper)

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Research

28 pages, 9848 KiB  
Article
Efficient One-Class False Data Detector Based on Deep SVDD for Smart Grids
Energies 2023, 16(20), 7069; https://doi.org/10.3390/en16207069 - 12 Oct 2023
Viewed by 850
Abstract
In the smart grid, malicious consumers can hack their smart meters to report false power consumption readings to steal electricity. Developing a machine-learning based detector for identifying these readings is a challenge due to the unavailability of malicious datasets. Most of the existing [...] Read more.
In the smart grid, malicious consumers can hack their smart meters to report false power consumption readings to steal electricity. Developing a machine-learning based detector for identifying these readings is a challenge due to the unavailability of malicious datasets. Most of the existing works in the literature assume attacks to compute malicious data. These detectors are trained to identify these attacks, but they cannot identify new attacks, which creates a vulnerability. Very few papers in the literature tried to address this problem by investigating anomaly detectors trained solely on benign data, but they suffer from these limitations: (1) low detection accuracy and high false alarm; (2) the need for knowledge on the malicious data to compute good detection thresholds; and (3) they cannot capture the temporal correlations of the readings and do not address the class overlapping issue caused by some deceptive attacks. To address these limitations, this paper presents a deep support vector data description (DSVDD) based unsupervised detector for false data in smart grid. Time-series readings are transformed into images, and the detector is exclusively trained on benign images. Our experimental results demonstrate the superior performance of our detectors compared to existing approaches in the literature. Specifically, our proposed DSVDD-based schemes have exhibited improvements of 0.5% to 3% in terms of recall and 3% to 9% in terms of the Area Under the Curve (AUC) when compared to existing state-of-the-art detectors. Full article
(This article belongs to the Special Issue The Future of Cyber Security in Smart Grids)
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