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Cybersecurity and Privacy-Preserving in Modern Smart Grid

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

Deadline for manuscript submissions: closed (20 October 2021) | Viewed by 37324

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Special Issue Editors

Dr. Panagiotis Sarigiannidis

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Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Interests: IoT; 5G mobile communication; UAV; quality of service; radio access networks; computer network security; radio networks; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Thomas Lagkas

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Guest Editor

Department of Computer Science, International Hellenic University, 65404 Kavala, Greece
Interests: IoT; 5G and beyond
Special Issues, Collections and Topics in MDPI journals

Dr. Konstantinos Rantos

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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, Collections and Topics in MDPI journals

Dr. Francisco Ramos

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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 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.

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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.

Published Papers (10 papers)

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Research

19 pages, 1888 KiB

Open AccessArticle

Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems

by Zakaria El Mrabet, Niroop Sugunaraj, Prakash Ranganathan and Shrirang Abhyankar

Sensors 2022, 22(2), 458; https://doi.org/10.3390/s22020458 - 08 Jan 2022

Cited by 23 | Viewed by 3246

Abstract

Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time. Full article

(This article belongs to the Special Issue Cybersecurity and Privacy-Preserving in Modern Smart Grid)

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18 pages, 3676 KiB

Open AccessArticle

Cyber-Physical Vulnerability Assessment in Smart Grids Based on Multilayer Complex Networks

by Monica Alonso, Jaime Turanzas, Hortensia Amaris and Angel T. Ledo

Sensors 2021, 21(17), 5826; https://doi.org/10.3390/s21175826 - 30 Aug 2021

Cited by 10 | Viewed by 2107

Abstract

In the last decade, the main attacks against smart grids have occurred in communication networks (ITs) causing the disconnection of physical equipment from power networks (OTs) and leading to electricity supply interruptions. To deal with the deficiencies presented in past studies, this paper addresses smart grids vulnerability assessment considering the smart grid as a cyber-physical heterogeneous interconnected system. The model of the cyber-physical system is composed of a physical power network model and the information and communication technology network model (ICT) both are interconnected and are interrelated by means of the communication and control equipment installed in the smart grid. This model highlights the hidden interdependencies between power and ICT networks and contains the interaction between both systems. To mimic the real nature of smart grids, the interconnected heterogeneous model is based on multilayer complex network theory and scale-free graph, where there is a one-to-many relationship between cyber and physical assets. Multilayer complex network theory centrality indexes are used to determine the interconnected heterogeneous system set of nodes criticality. The proposed methodology, which includes measurement, communication, and control equipment, has been tested on a standardized power network that is interconnected to the ICT network. Results demonstrate the model’s effectiveness in detecting vulnerabilities in the interdependent cyber-physical system compared to traditional vulnerability assessments applied to power networks (OT). Full article

(This article belongs to the Special Issue Cybersecurity and Privacy-Preserving in Modern Smart Grid)

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21 pages, 1373 KiB

Open AccessArticle

P4G2Go: A Privacy-Preserving Scheme for Roaming Energy Consumers of the Smart Grid-to-Go

by Aristeidis Farao, Eleni Veroni, Christoforos Ntantogian and Christos Xenakis

Sensors 2021, 21(8), 2686; https://doi.org/10.3390/s21082686 - 11 Apr 2021

Cited by 19 | Viewed by 2656

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 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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27 pages, 1395 KiB

Open AccessArticle

Rule-Based Detection of False Data Injections Attacks against Optimal Power Flow in Power Systems

by Sani Umar and Muhamad Felemban

Sensors 2021, 21(7), 2478; https://doi.org/10.3390/s21072478 - 02 Apr 2021

Cited by 6 | Viewed by 3571

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 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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23 pages, 833 KiB

Open AccessArticle

Identifying and Analyzing Dependencies in and among Complex Cyber Physical Systems

by Aida Akbarzadeh and Sokratis Katsikas

Sensors 2021, 21(5), 1685; https://doi.org/10.3390/s21051685 - 01 Mar 2021

Cited by 11 | Viewed by 2466

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 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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15 pages, 1843 KiB

Open AccessArticle

End-to-End Deep Graph Convolutional Neural Network Approach for Intentional Islanding in Power Systems Considering Load-Generation Balance

by Zhonglin Sun, Yannis Spyridis, Thomas Lagkas, Achilleas Sesis, Georgios Efstathopoulos and Panagiotis Sarigiannidis

Sensors 2021, 21(5), 1650; https://doi.org/10.3390/s21051650 - 27 Feb 2021

Cited by 8 | Viewed by 2639

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 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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20 pages, 8907 KiB

Open AccessArticle

Vulnerability and Impact Analysis of the IEC 61850 GOOSE Protocol in the Smart Grid

by Haftu Tasew Reda, Biplob Ray, Pejman Peidaee, Adnan Anwar, Abdun Mahmood, Akhtar Kalam and Nahina Islam

Sensors 2021, 21(4), 1554; https://doi.org/10.3390/s21041554 - 23 Feb 2021

Cited by 27 | Viewed by 5729

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 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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20 pages, 2258 KiB

Open AccessArticle

Dynamic Adaptive Cross-Chain Trading Mode for Multi-Microgrid Joint Operation

by Longze Wang, Jing Wu, Rongfang Yuan, Delong Zhang, Jinxin Liu, Siyu Jiang, Yan Zhang and Meicheng Li

Sensors 2020, 20(21), 6096; https://doi.org/10.3390/s20216096 - 27 Oct 2020

Cited by 19 | Viewed by 3150

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. 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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20 pages, 1096 KiB

Open AccessEditor’s ChoiceArticle

ARIES: A Novel Multivariate Intrusion Detection System for Smart Grid

by Panagiotis Radoglou Grammatikis, Panagiotis Sarigiannidis, Georgios Efstathopoulos and Emmanouil Panaousis

Sensors 2020, 20(18), 5305; https://doi.org/10.3390/s20185305 - 16 Sep 2020

Cited by 32 | Viewed by 4737

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 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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24 pages, 561 KiB

Open AccessArticle

Game Theoretic Honeypot Deployment in Smart Grid

by Panagiotis Diamantoulakis, Christos Dalamagkas, Panagiotis Radoglou-Grammatikis, Panagiotis Sarigiannidis and George Karagiannidis

Sensors 2020, 20(15), 4199; https://doi.org/10.3390/s20154199 - 28 Jul 2020

Cited by 14 | Viewed by 4414

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 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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