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Smart Grid Cybersecurity: Challenges, Threats and Solutions

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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 (10 April 2023) | Viewed by 15878

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Special Issue Editors

Dr. Basile L. Agba

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Guest Editor

Hydro-Quebec Research Institute, Varennes, PQ G1A 1A1, Canada
Interests: smart grid; impulse noise; smart power grids; power engineering computing; wireless sensor networks; Gaussian processes; markov processes; carrier transmission on power lines; interference suppression; least mean squares methods; power system measurement; power system security
Special Issues, Collections and Topics in MDPI journals

Dr. Marthe Kassouf

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Guest Editor

Institut de recherche d'Hydro-Quebec, Varennes, QC 113259, Canada
Interests: cybersecurity of power grids and critical infrastructures, security data analytics; computer networks; time synchronization systems; smart grid communications; wireless communications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mourad Debbabi

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Guest Editor

Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC 5618, Canada
Interests: cyber threat intelligence; smart grid security; cybersecurity and privacy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The next generation of Electric Grids relies on an invasive deployment of communication and information technologies (IT) in multiple systems spread across the large infrastructure that interconnects the consumer premises and the electricity distribution, tranmission and generation facilities. Labeled as grid digitization, this movement has been transforming the grid automation and control systems, thus, making data and telecommunications an integral part of operational technologies (OT) and establishing the concept of IT/OT convergence. Grid digitization enables operators to have better control and monitoring of the power grid components and allows improved maintenance prediction, better management and integration of distributed energy resources (DERs) as well as enhanced customer services. However, significant challenges arise, namely, ensuring efficient system integration and standardization and restricting the power grid exposure to cyber threats.

Recent reports confirm that cyberattacks targeting power grids and other critical infrastructures have been increasing in frequency and severity. In this context, smart grid operators and the elecriticity industry stakeholders are required to design and implement novel solutions to enhancethe grid resilience and the capability to detect, neutralize and respond to cyberattacks.

This special issue provides a platform for researchers, engineers, technicians and other stakeholders from engineering and cybersecurity communities to propose relevant solutions.

The proposed papers consist of novel and original ideas and results, theoretical and applied research in the following topics, but not limited to:

Smart grid risk management
Security metrics and resilience assessment
Security policy development
Cyberattack simulation and case studies
Detection and mitigation of cyberattacks
Cybersecurity investments and the economic impact of cyberattacks
Privacy challenges

Dr. Basile L. Agba
Dr. Marthe Kassouf
Prof. Dr. Mourad Debbabi
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

smart grid
cybersecurity of power systems
IT/OT convergence
cyberattack simulation and impact analysis
cybersecurity data analytics
IoT integration and security

Related Special Issue

Smart Grid Cybersecurity: Challenges, Threats and Solutions—2nd Edition in Energies (1 article)

Published Papers (5 papers)

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Research

19 pages, 3528 KiB

Open AccessArticle

Detection of Load-Altering Cyberattacks Targeting Peak Shaving Using Residential Electric Water Heaters

by El-Nasser S. Youssef, Fabrice Labeau and Marthe Kassouf

Energies 2022, 15(20), 7807; https://doi.org/10.3390/en15207807 - 21 Oct 2022

Cited by 2 | Viewed by 1419

Abstract

The rapid adoption of the smart grid’s nascent load-management capabilities, such as demand-side management and smart home systems, and the emergence of new classes of controllable high-wattage loads, such as energy storage systems and electric vehicles, magnify the smart grid’s exposure to load-altering cyberattacks. These attacks aim at disrupting power grid services by staging a synchronized activation/deactivation of numerous customers’ high-wattage appliances. A proper defense plan is needed to respond to such attacks and maintain the stability of the grid, and would include prevention, detection, mitigation, incident response, and/or recovery strategies. In this paper, we propose a solution to detect load-altering cyberattacks using a time-delay neural network that monitors the grid’s load profile. As a case study, we consider a cyberattack scenario against demand-side management programs that control the loads of residential electrical water heaters in order to perform peak shaving. The proposed solution can be adapted to other load-altering attacks involving different demand-side management programs or other classes of loads. Experiments verify the proposed solution’s efficacy in detecting load-altering attacks with high precision and low false alarm and latency. Full article

(This article belongs to the Special Issue Smart Grid Cybersecurity: Challenges, Threats and Solutions)

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37 pages, 3880 KiB

Open AccessArticle

Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions

by Jianguo Ding, Attia Qammar, Zhimin Zhang, Ahmad Karim and Huansheng Ning

Energies 2022, 15(18), 6799; https://doi.org/10.3390/en15186799 - 17 Sep 2022

Cited by 28 | Viewed by 6555

Abstract

Smart Grids (SGs) are governed by advanced computing, control technologies, and networking infrastructure. However, compromised cybersecurity of the smart grid not only affects the security of existing energy systems but also directly impacts national security. The increasing number of cyberattacks against the smart grid urgently necessitates more robust security protection technologies to maintain the security of the grid system and its operations. The purpose of this review paper is to provide a thorough understanding of the incumbent cyberattacks’ influence on the entire smart grid ecosystem. In this paper, we review the various threats in the smart grid, which have two core domains: the intrinsic vulnerability of the system and the external cyberattacks. Similarly, we analyze the vulnerabilities of all components of the smart grid (hardware, software, and data communication), data management, services and applications, running environment, and evolving and complex smart grids. A structured smart grid architecture and global smart grid cyberattacks with their impact from 2010 to July 2022 are presented. Then, we investigated the the thematic taxonomy of cyberattacks on smart grids to highlight the attack strategies, consequences, and related studies analyzed. In addition, potential cybersecurity solutions to smart grids are explained in the context of the implementation of blockchain and Artificial Intelligence (AI) techniques. Finally, technical future directions based on the analysis are provided against cyberattacks on SGs. Full article

(This article belongs to the Special Issue Smart Grid Cybersecurity: Challenges, Threats and Solutions)

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17 pages, 3329 KiB

Open AccessArticle

False Data Injection Attack Detection in Smart Grid Using Energy Consumption Forecasting

by Abrar Mahi-al-rashid, Fahmid Hossain, Adnan Anwar and Sami Azam

Energies 2022, 15(13), 4877; https://doi.org/10.3390/en15134877 - 02 Jul 2022

Cited by 18 | Viewed by 2870

Abstract

Supervisory Control and Data Acquisition (SCADA) systems are essential for reliable communication and control of smart grids. However, in the cyber-physical realm, it becomes highly vulnerable to cyber-attacks like False Data Injection (FDI) into the measurement signal which can circumvent the conventional detection methods and interfere with the normal operation of grids, which in turn could potentially lead to huge financial losses and can have a large impact on public safety. It is imperative to have an accurate state estimation of power consumption for further operational decision-making.This work presents novel forecasting-aided anomaly detection using an CNN-LSTM based auto-encoder sequence to sequence architecture to combat against false data injection attacks. We further present an adaptive optimal threshold based on the consumption patterns to identify abnormal behaviour. Evaluation is performed on real-time energy demand consumption data collected from the Australian Energy Market Operator. An extensive experiment shows that the proposed model outperforms other benchmark algorithms in not only improving the data injection attack (95.43%) but also significantly reducing the false positive rate. Full article

(This article belongs to the Special Issue Smart Grid Cybersecurity: Challenges, Threats and Solutions)

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19 pages, 3400 KiB

Open AccessArticle

An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid Protection

by Konstantinos Demertzis, Dimitrios Taketzis, Vasiliki Demertzi and Charalabos Skianis

Energies 2022, 15(12), 4398; https://doi.org/10.3390/en15124398 - 16 Jun 2022

Cited by 2 | Viewed by 1461

Abstract

The rate of technical innovation, system interconnection, and advanced communications undoubtedly boost distributed energy networks’ efficiency. However, when an additional attack surface is made available, the possibility of an increase in attacks is an unavoidable result. The energy ecosystem’s significant variety draws attackers with various goals, making any critical infrastructure a threat, regardless of scale. Outdated technology and other antiquated countermeasures that worked years ago cannot address the complexity of current threats. As a result, robust artificial intelligence cyber-defense solutions are more important than ever. Based on the above challenge, this paper proposes an ensemble transfer learning spiking immune system for adaptive smart grid protection. It is an innovative Artificial Immune System (AIS) that uses a swarm of Evolving Izhikevich Neural Networks (EINN) in an Ensemble architecture, which optimally integrates Transfer Learning methodologies. The effectiveness of the proposed innovative system is demonstrated experimentally in multiple complex scenarios that optimally simulate the modern energy environment. The most significant findings of this work are that the transfer learning architecture’s shared learning rate significantly adds to the speed of generalization and convergence approach. In addition, the ensemble combination improves the accuracy of the model because the overall behavior of the numerous models is less noisy than a comparable individual single model. Finally, the Izhikevich Spiking Neural Network used here, due to its dynamic configuration, can reproduce different spikes and triggering behaviors of neurons, which models precisely the problem of digital security of energy infrastructures, as proved experimentally. Full article

(This article belongs to the Special Issue Smart Grid Cybersecurity: Challenges, Threats and Solutions)

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19 pages, 1795 KiB

Open AccessArticle

Heuristic Intrusion Detection Based on Traffic Flow Statistical Analysis

by Wojciech Szczepanik and Marcin Niemiec

Energies 2022, 15(11), 3951; https://doi.org/10.3390/en15113951 - 27 May 2022

Cited by 5 | Viewed by 1992

Abstract

As telecommunications are becoming increasingly important for modern systems, ensuring secure data transmission is getting more and more critical. Specialised numerous devices that form smart grids are a potential attack vector and therefore is a challenge for cybersecurity. It requires the continuous development of methods to counteract this risk. This paper presents a heuristic approach to detecting threats in network traffic using statistical analysis of packet flows. The important advantage of this method is ability of intrusion detection also in encrypted transmissions. Flow information is processing by neural networks to detect malicious traffic. The architectures of subsequent versions of the artificial neural networks were generated based on the results obtained by previous iterations by searching the hyperparameter space, resulting in more refined models. Finally, the networks prepared in this way exhibited high performance while maintaining a small size—thereby making them an effective method of attacks detection in network environment to protect smart grids. Full article

(This article belongs to the Special Issue Smart Grid Cybersecurity: Challenges, Threats and Solutions)

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