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Special Issue "Cybersecurity and Privacy-Preserving in Modern Smart Grid"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 20 October 2021.

Special Issue Editors

Dr. Panagiotis Sarigiannidis
E-Mail Website
Guest Editor
Department of Informatics and Telecommunications Engineering, University of Western Macedonia, 50100 Kozani, Greece
Interests: telecommunication networks; Internet of Things; cybersecurity
Special Issues and Collections in MDPI journals
Prof. Dr. Konstantinos Rantos
E-Mail Website
Guest Editor
Department of Computer Science, School of Sciences, International Hellenic University, Ag. Loukas Campus, 65404 Kavala, Greece
Interests: cybersecurity; IoT security; cyber threat intelligence; authentication systems; e-Government services; electronic payment systems; mobile systems security; security awareness
Special Issues and Collections in MDPI journals
Dr. Francisco Ramos
E-Mail Website
Guest Editor
Schneider Electric, Charles Darwin s/n, Edificio Bogaris, 41092 Sevilla, Spain
Interests: smart grids; cybersecurity; renewables; smart cities; Internet of the things and embedded systems

Special Issue Information

Dear Colleagues,

The need for an energy transition in Europe, and worldwide, is becoming major, and is faced by significant and far-reaching challenges. More than ever, transportation, communications, resource management (water and air), and even agriculture are enabled by modern electrical power and energy systems (EPESs) promoting automation. It is clear that energy is going more to be electrical and this is a great chance to integrate a higher share of renewables, promoting a more efficient and decentralized energy system, by involving advanced digital technologies and systems such as smart devices, faster and more flexible gateways, smart meters, and Internet of things (IoT). However, this transition comes with a significant cost: The need for cyber-defense measures, strategies, algorithms, schemes, tools, and frameworks to maintain or improve the infrastructure’s security posture.

The electric smart grid (ESG) is a modern EPES. This endeavor constitutes the evolution of the traditional electric grid, focusing on generating and conditioning electricity, while efficiently distributing, controlling, and monitoring it in real-time. Being beneficial not only for power industries, but also for consumers, ESGs also aim to preserve information privacy and offer protection against intrusions. However, due to their critical nature, vast scale and their expanded attack surface, ESGs are bound to face existing and evolving cyberthreats targeting vulnerable deployments. Recently ESG infrastructures have faced several cyberattacks that have raised questions regarding security inefficiencies and their large impact on system robustness, productiveness, and integrity.

This Special Issue seeks to make an in-depth, critical contribution to this evolving field of cybersecurity in EPES. In the context of this Special Issue, we intend to bring together state-of-the-art research contributions providing new insights in securing the EPES from data breaches, managing threats, preventing and detecting cyber intrusions, and preserving sensitive and private information. The topics that can be addressed include (but are not limited to) the following:

  • Intrusion detection systems and big data analytics for accurate anomaly detection for smart grids.
  • Cybersecurity mechanisms, tools, and frameworks in modern smart grids.
  • Anonymity in modern smart grids.
  • Privacy-preserving tools, frameworks, and schemes in modern smart grids.
  • Security information and event management in modern smart grids.
  • Privacy standards and certificates for smart grids and energy networks.
  • State of the art privacy-preserving mechanisms and techniques in smart grids and energy networks.
  • Modern and advanced access control schemes for modern smart grids for safeguarding energy network reliability and integrity.
  • Blockchain technologies for accessing and sharing energy data in modern smart grids.
  • Anonymous communication channels in smart grids and energy networks.
  • Trust management and mechanisms in modern smart grids.
  • Recent cybersecurity incidents and data breaches in smart grids and energy domains.
  • Security certification processes in electric smart grids.
  • GDPR-compliant mechanisms and schemes in critical infrastructure and modern smart grids.
  • Security information and event management tools for critical infrastructure and modern smart grids.
  • Light encryption and new cryptography methods in electrical power and energy systems.
  • Homomorphic encryption in modern smart grids and energy systems.
  • Big data analytics, machine learning tools, and deep learning techniques for anomaly detection in smart grids and energy systems.
  • Cyber threat intelligence management and sharing in smart grids.
  • Risk management in modern EPESs.
  • Security and privacy by design in smart grids.
  • Security metrics and evaluation in smart grids.
  • Deception mechanisms in smart grids.

Dr. Panagiotis Sarigiannidis
Dr. Thomas Lagkas
Dr. Konstantinos Rantos
Dr. Francisco Ramos
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 papers will be 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. Sensors 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 2200 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.

Published Papers (8 papers)

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Research

Article
P4G2Go: A Privacy-Preserving Scheme for Roaming Energy Consumers of the Smart Grid-to-Go
Sensors 2021, 21(8), 2686; https://doi.org/10.3390/s21082686 - 11 Apr 2021
Viewed by 508
Abstract
Due to its flexibility in terms of charging and billing, the smart grid is an enabler of many innovative energy consumption scenarios. One such example is when a landlord rents their property for a specific period to tenants. Then the electricity bill could [...] Read more.
Due to its flexibility in terms of charging and billing, the smart grid is an enabler of many innovative energy consumption scenarios. One such example is when a landlord rents their property for a specific period to tenants. Then the electricity bill could be redirected from the landlord’s utility to the tenant’s utility. This novel scenario of the smart grid ecosystem, defined in this paper as Grid-to-Go (G2Go), promotes a green economy and can drive rent reductions. However, it also creates critical privacy issues, since utilities may be able to track the tenant’s activities. This paper presents P4G2Go, a novel privacy-preserving scheme that provides strong security and privacy assertions for roaming consumers against honest but curious entities of the smart grid. At the heart of P4G2Go lies the Idemix cryptographic protocol suite, which utilizes anonymous credentials and provides unlinkability of the consumer activities. Our scheme is complemented by the MASKER protocol, used to protect the consumption readings, and the FIDO2 protocol for strong and passwordless authentication. We have implemented the main components of P4G2Go, to quantitatively assess its performance. Finally, we reason about its security and privacy properties, proving that P4G2Go achieves to fulfill the relevant objectives. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy-Preserving in Modern Smart Grid)
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Article
Rule-Based Detection of False Data Injections Attacks against Optimal Power Flow in Power Systems
Sensors 2021, 21(7), 2478; https://doi.org/10.3390/s21072478 - 02 Apr 2021
Viewed by 687
Abstract
Cyber-security of modern power systems has captured a significant interest. The vulnerabilities in the cyber infrastructure of the power systems provide an avenue for adversaries to launch cyber attacks. An example of such cyber attacks is False Data Injection Attacks (FDIA). The main [...] Read more.
Cyber-security of modern power systems has captured a significant interest. The vulnerabilities in the cyber infrastructure of the power systems provide an avenue for adversaries to launch cyber attacks. An example of such cyber attacks is False Data Injection Attacks (FDIA). The main contribution of this paper is to analyze the impact of FDIA on the cost of power generation and the physical component of the power systems. Furthermore, We introduce a new FDIA strategy that intends to maximize the cost of power generation. The viability of the attack is shown using simulations on the standard IEEE bus systems using the MATPOWER MATLAB package. We used the genetic algorithm (GA), simulated annealing (SA) algorithm, tabu search (TS), and particle swarm optimization (PSO) to find the suitable attack targets and execute FDIA in the power systems. The proposed FDIA increases the generation cost by up to 15.6%, 45.1%, 60.12%, and 74.02% on the 6-bus, 9-bus, 30-bus, and 118-bus systems, respectively. Finally, a rule-based FDIA detection and prevention mechanism is proposed to mitigate such attacks on power systems. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy-Preserving in Modern Smart Grid)
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Article
Identifying and Analyzing Dependencies in and among Complex Cyber Physical Systems
Sensors 2021, 21(5), 1685; https://doi.org/10.3390/s21051685 - 01 Mar 2021
Viewed by 483
Abstract
Contemporary Critical Infrastructures (CIs), such as the power grid, comprise cyber physical systems that are tightly coupled, to form a complex system of interconnected components with interacting dependencies. Modelling methodologies have been suggested as proper tools to provide better insight into the dependencies [...] Read more.
Contemporary Critical Infrastructures (CIs), such as the power grid, comprise cyber physical systems that are tightly coupled, to form a complex system of interconnected components with interacting dependencies. Modelling methodologies have been suggested as proper tools to provide better insight into the dependencies and behavioural characteristics of these complex systems. In order to facilitate the study of interconnections in and among critical infrastructures, and to provide a clear view of the interdependencies among their cyber and physical components, this paper proposes a novel method, based on a graphical model called Modified Dependency Structure Matrix (MDSM). The MDSM provides a compact perspective of both inter-dependency and intra-dependency between subsystems of one complex system or two distinct systems. Additionally, we propose four parameters that allow the quantitative assessment of the characteristics of dependencies, including multi-order dependencies in large scale CIs. We illustrate the workings of the proposed method by applying it to a micro-distribution network based on the G2ELAB 14-Bus model. The results provide valuable insight into the dependencies among the network components and substantiate the applicability of the proposed method for analyzing large scale cyber physical systems. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy-Preserving in Modern Smart Grid)
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Article
End-to-End Deep Graph Convolutional Neural Network Approach for Intentional Islanding in Power Systems Considering Load-Generation Balance
Sensors 2021, 21(5), 1650; https://doi.org/10.3390/s21051650 - 27 Feb 2021
Viewed by 566
Abstract
Intentional islanding is a corrective procedure that aims to protect the stability of the power system during an emergency, by dividing the grid into several partitions and isolating the elements that would cause cascading failures. This paper proposes a deep learning method to [...] Read more.
Intentional islanding is a corrective procedure that aims to protect the stability of the power system during an emergency, by dividing the grid into several partitions and isolating the elements that would cause cascading failures. This paper proposes a deep learning method to solve the problem of intentional islanding in an end-to-end manner. Two types of loss functions are examined for the graph partitioning task, and a loss function is added on the deep learning model, aiming to minimise the load-generation imbalance in the formed islands. In addition, the proposed solution incorporates a technique for merging the independent buses to their nearest neighbour in case there are isolated buses after the clusterisation, improving the final result in cases of large and complex systems. Several experiments demonstrate that the introduced deep learning method provides effective clustering results for intentional islanding, managing to keep the power imbalance low and creating stable islands. Finally, the proposed method is dynamic, relying on real-time system conditions to calculate the result. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy-Preserving in Modern Smart Grid)
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Article
Vulnerability and Impact Analysis of the IEC 61850 GOOSE Protocol in the Smart Grid
Sensors 2021, 21(4), 1554; https://doi.org/10.3390/s21041554 - 23 Feb 2021
Cited by 1 | Viewed by 872
Abstract
IEC 61850 is one of the most prominent communication standards adopted by the smart grid community due to its high scalability, multi-vendor interoperability, and support for several input/output devices. Generic Object-Oriented Substation Events (GOOSE), which is a widely used communication protocol defined in [...] Read more.
IEC 61850 is one of the most prominent communication standards adopted by the smart grid community due to its high scalability, multi-vendor interoperability, and support for several input/output devices. Generic Object-Oriented Substation Events (GOOSE), which is a widely used communication protocol defined in IEC 61850, provides reliable and fast transmission of events for the electrical substation system. This paper investigates the security vulnerabilities of this protocol and analyzes the potential impact on the smart grid by rigorously analyzing the security of the GOOSE protocol using an automated process and identifying vulnerabilities in the context of smart grid communication. The vulnerabilities are tested using a real-time simulation and industry standard hardware-in-the-loop emulation. An in-depth experimental analysis is performed to demonstrate and verify the security weakness of the GOOSE publish-subscribe protocol towards the substation protection within the smart grid setup. It is observed that an adversary who might have familiarity with the substation network architecture can create falsified attack scenarios that can affect the physical operation of the power system. Extensive experiments using the real-time testbed validate the theoretical analysis, and the obtained experimental results prove that the GOOSE-based IEC 61850 compliant substation system is vulnerable to attacks from malicious intruders. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy-Preserving in Modern Smart Grid)
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Article
Dynamic Adaptive Cross-Chain Trading Mode for Multi-Microgrid Joint Operation
Sensors 2020, 20(21), 6096; https://doi.org/10.3390/s20216096 - 27 Oct 2020
Cited by 2 | Viewed by 638
Abstract
The emerging blockchain technology has injected new vitality into the energy market, especially the peer-to-peer power trading of microgrid systems. However, with the increase of energy blockchain projects, the difficulty of data communication and value islands between blockchain networks have become open issues. [...] Read more.
The emerging blockchain technology has injected new vitality into the energy market, especially the peer-to-peer power trading of microgrid systems. However, with the increase of energy blockchain projects, the difficulty of data communication and value islands between blockchain networks have become open issues. Thus, in this paper, we propose a dynamic adaptive cross-chain trading mode for multi-microgrid joint operation. The novelty is to design a proof of credit threshold consensus mechanism to achieve effective information verification. This consensus mechanism can ensure the adaptive consistency of cross-chain information without changing the existing blockchain architecture of each system. At the same time, we design a corresponding key management interoperability protocol based on RSA algorithm and Chinese remainder theorem, which can realize data transfer and information consensus for cross-chain transactions. The theoretical analysis verifies that the cross-chain communication information is effective and the system is able to protect against the attack of malicious nodes. Finally, a cross-chain simulation experiment is established to analyze the operation efficiency. The result shows that this cross-chain trading takes place within seconds, which basically meets the response requirements for multi-microgrid joint operation. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy-Preserving in Modern Smart Grid)
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Article
ARIES: A Novel Multivariate Intrusion Detection System for Smart Grid
Sensors 2020, 20(18), 5305; https://doi.org/10.3390/s20185305 - 16 Sep 2020
Cited by 5 | Viewed by 857
Abstract
The advent of the Smart Grid (SG) raises severe cybersecurity risks that can lead to devastating consequences. In this paper, we present a novel anomaly-based Intrusion Detection System (IDS), called ARIES (smArt gRid Intrusion dEtection System), which is capable of protecting efficiently SG [...] Read more.
The advent of the Smart Grid (SG) raises severe cybersecurity risks that can lead to devastating consequences. In this paper, we present a novel anomaly-based Intrusion Detection System (IDS), called ARIES (smArt gRid Intrusion dEtection System), which is capable of protecting efficiently SG communications. ARIES combines three detection layers that are devoted to recognising possible cyberattacks and anomalies against (a) network flows, (b) Modbus/Transmission Control Protocol (TCP) packets and (c) operational data. Each detection layer relies on a Machine Learning (ML) model trained using data originating from a power plant. In particular, the first layer (network flow-based detection) performs a supervised multiclass classification, recognising Denial of Service (DoS), brute force attacks, port scanning attacks and bots. The second layer (packet-based detection) detects possible anomalies related to the Modbus packets, while the third layer (operational data based detection) monitors and identifies anomalies upon operational data (i.e., time series electricity measurements). By emphasising on the third layer, the ARIES Generative Adversarial Network (ARIES GAN) with novel error minimisation functions was developed, considering mainly the reconstruction difference. Moreover, a novel reformed conditional input was suggested, consisting of random noise and the signal features at any given time instance. Based on the evaluation analysis, the proposed GAN network overcomes the efficacy of conventional ML methods in terms of Accuracy and the F1 score. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy-Preserving in Modern Smart Grid)
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Article
Game Theoretic Honeypot Deployment in Smart Grid
Sensors 2020, 20(15), 4199; https://doi.org/10.3390/s20154199 - 28 Jul 2020
Cited by 1 | Viewed by 825
Abstract
The smart grid provides advanced functionalities, including real-time monitoring, dynamic energy management, advanced pricing mechanisms, and self-healing, by enabling the two-way flow of power and data, as well as the use of Internet of Things (IoT) technologies and devices. However, converting the traditional [...] Read more.
The smart grid provides advanced functionalities, including real-time monitoring, dynamic energy management, advanced pricing mechanisms, and self-healing, by enabling the two-way flow of power and data, as well as the use of Internet of Things (IoT) technologies and devices. However, converting the traditional power grids to smart grids poses severe security challenges and makes their components and services prone to cyber attacks. To this end, advanced techniques are required to mitigate the impact of the potential attacks. In this paper, we investigate the use of honeypots, which are considered to mimic the common services of the smart grid and are able to detect unauthorized accesses, collect evidence, and help hide the real devices. More specifically, the interaction of an attacker and a defender is considered, who both optimize the number of attacks and the defending system configuration, i.e., the number of real devices and honeypots, respectively, with the aim to maximize their individual payoffs. To solve this problem, game theoretic tools are used, considering an one-shot game and a repeated game with uncertainty about the payoff of the attacker, where the Nash Equilibrium (NE) and the Bayesian NE are derived, respectively. Finally, simulation results are provided, which illustrate the effectiveness of the proposed framework. Full article
(This article belongs to the Special Issue Cybersecurity and Privacy-Preserving in Modern Smart Grid)
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