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Cybersecurity Issues in Smart Grids and Future Power Systems

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 51853

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Guest Editor
Electrical Power Engineering, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G1 1XQ, UK
Interests: high-voltage engineering; electricity markets; smart grids; power quality; power system design and operation
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Special Issue Information

Dear Colleagues,

The demand for a smart and intelligent power system is growing in tandem with the increased interest in renewable energy sources. This has resulted in the adoption of smart grids, which are electrical systems that leverage digital communication infrastructure. Smart grids have several advantages, including the potential to provide consumers with a continuous power supply, reduced line losses, enhanced renewable output and storage, consumer participation in electricity markets, and demand-side responsiveness. Future power systems, also known as smart grids, will rely more on renewable energy sources such as solar and wind, as well as storage. Power electronic converters are used in renewable energy generation and storage. Each converter/inverter manufacturer has its own algorithm for programming and optimising its hardware. Furthermore, to respond to any signal from the system operator, these converters rely on communication protocols. As a result, cyber-attacks on these smart converters/inverters are a concern. Despite the fact that numerous cyber–physical systems (CPS) have been presented, there is no universal CPS standard that can be employed with various types of converters. The main goal of this Special Issue is to give academics, researchers, and industry professionals an opportunity to highlight their current work and define future directions.

Topics of interest include, but are not limited to:

  • Advanced converter control algorithms;
  • Synthetic inertia and virtual synchronous machines;
  • Cyber–physical systems for power systems;
  • Power quality issues in future power systems;
  • Lightweight encryption methods;
  • Intrusion detection systems;
  • Machine learning for cybersecurity;
  • Secure and trustworthy operations in the industrial Internet of Things (IoT);
  • Cybersecurity issues in the IoT

Dr. Arshad Arshad
Guest Editor

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Keywords

  • advanced converter control algorithms
  • synthetic inertia and virtual synchronous machines
  • cyber–physical systems for power systems
  • power quality issues in future power systems
  • lightweight encryption methods
  • intrusion detection systems
  • machine learning for cybersecurity
  • secure and trustworthy operations in the industrial Internet of Things (IoT)
  • cybersecurity issues in the IoT

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Published Papers (11 papers)

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Research

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21 pages, 5695 KiB  
Article
Hybrid CNN–Transformer Network for Electricity Theft Detection in Smart Grids
by Yu Bai, Haitong Sun, Lili Zhang and Haoqi Wu
Sensors 2023, 23(20), 8405; https://doi.org/10.3390/s23208405 - 12 Oct 2023
Cited by 1 | Viewed by 1114
Abstract
Illicitly obtaining electricity, commonly referred to as electricity theft, is a prominent contributor to power loss. In recent years, there has been growing recognition of the significance of neural network models in electrical theft detection (ETD). Nevertheless, the existing approaches have a restricted [...] Read more.
Illicitly obtaining electricity, commonly referred to as electricity theft, is a prominent contributor to power loss. In recent years, there has been growing recognition of the significance of neural network models in electrical theft detection (ETD). Nevertheless, the existing approaches have a restricted capacity to acquire profound characteristics, posing a persistent challenge in reliably and effectively detecting anomalies in power consumption data. Hence, the present study puts forth a hybrid model that amalgamates a convolutional neural network (CNN) and a transformer network as a means to tackle this concern. The CNN model with a dual-scale dual-branch (DSDB) structure incorporates inter- and intra-periodic convolutional blocks to conduct shallow feature extraction of sequences from varying dimensions. This enables the model to capture multi-scale features in a local-to-global fashion. The transformer module with Gaussian weighting (GWT) effectively captures the overall temporal dependencies present in the electricity consumption data, enabling the extraction of sequence features at a deep level. Numerous studies have demonstrated that the proposed method exhibits enhanced efficiency in feature extraction, yielding high F1 scores and AUC values, while also exhibiting notable robustness. Full article
(This article belongs to the Special Issue Cybersecurity Issues in Smart Grids and Future Power Systems)
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26 pages, 5741 KiB  
Article
Intelligent Sensors for dc Fault Location Scheme Based on Optimized Intelligent Architecture for HVdc Systems
by Muhammad Zain Yousaf, Muhammad Faizan Tahir, Ali Raza, Muhammad Ahmad Khan and Fazal Badshah
Sensors 2022, 22(24), 9936; https://doi.org/10.3390/s22249936 - 16 Dec 2022
Cited by 7 | Viewed by 1485
Abstract
We develop a probabilistic model for determining the location of dc-link faults in MT-HVdc networks using discrete wavelet transforms (DWTs), Bayesian optimization, and multilayer artificial neural networks (ANNs) based on local information. Likewise, feedforward neural networks (FFNNs) are trained using the Levenberg–Marquardt backpropagation [...] Read more.
We develop a probabilistic model for determining the location of dc-link faults in MT-HVdc networks using discrete wavelet transforms (DWTs), Bayesian optimization, and multilayer artificial neural networks (ANNs) based on local information. Likewise, feedforward neural networks (FFNNs) are trained using the Levenberg–Marquardt backpropagation (LMBP) method, which multi-stage BO optimizes for efficiency. During training, the feature vectors at the sending terminal of the dc link are selected based on the norm values of the observed waveforms at various frequency bands. The multilayer ANN is trained using a comprehensive set of offline data that takes the denoising scheme into account. This choice not only helps to reduce the computational load but also provides better accuracy. An overall percentage error of 0.5144% is observed for the proposed algorithm when tested against fault resistances ranging from 10 to 485 Ω. The simulation results show that the proposed method can accurately estimate the fault site to a precision of 485 Ω and is more robust. Full article
(This article belongs to the Special Issue Cybersecurity Issues in Smart Grids and Future Power Systems)
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18 pages, 1141 KiB  
Article
Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing
by Shoaib Munawar, Nadeem Javaid, Zeshan Aslam Khan, Naveed Ishtiaq Chaudhary, Muhammad Asif Zahoor Raja, Ahmad H. Milyani and Abdullah Ahmed Azhari
Sensors 2022, 22(20), 7818; https://doi.org/10.3390/s22207818 - 14 Oct 2022
Cited by 11 | Viewed by 2551
Abstract
In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples’ nature. To tackle the problem of the [...] Read more.
In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples’ nature. To tackle the problem of the defused data, a Tomek Links technique targets the cross-pair majority class and is removed, which results in an affine-segregated decision boundary. In order to cope with a Theft Case scenario, theft data is ascertained and synthesized randomly by using six theft data variants. Theft data variants are benign class appertaining data samples which are modified and manipulated to synthesize malicious samples. Furthermore, a K-means minority oversampling technique is used to tackle the class imbalance issue. In addition, to enhance the detection of the classifier, abstract features are engineered using a stochastic feature engineering mechanism. Moreover, to carry out affine training of the model, balanced data are inputted in order to mitigate class imbalance issues. An integrated hybrid model consisting of Bi-Directional Gated Recurrent Units and Bi-Directional Long-Term Short-Term Memory classifies the consumers, efficiently. Afterwards, robustness performance of the model is verified using an attack vector which is subjected to intervene in the model’s efficiency and integrity. However, the proposed model performs efficiently on such unseen attack vectors. Full article
(This article belongs to the Special Issue Cybersecurity Issues in Smart Grids and Future Power Systems)
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30 pages, 11108 KiB  
Article
A Proactive Protection of Smart Power Grids against Cyberattacks on Service Data Transfer Protocols by Computational Intelligence Methods
by Igor Kotenko, Igor Saenko, Oleg Lauta and Alexander Kribel
Sensors 2022, 22(19), 7506; https://doi.org/10.3390/s22197506 - 3 Oct 2022
Cited by 2 | Viewed by 1536
Abstract
The article discusses an approach to the construction and operation of a proactive system for protecting smart power grids against cyberattacks on service data transfer protocols. It is based on a combination of computational intelligence methods: identifying anomalies in network traffic by evaluating [...] Read more.
The article discusses an approach to the construction and operation of a proactive system for protecting smart power grids against cyberattacks on service data transfer protocols. It is based on a combination of computational intelligence methods: identifying anomalies in network traffic by evaluating its self-similarity, detecting and classifying cyberattacks in anomalies, and taking effective protection measures using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells. Fractal analysis, mathematical statistics, and neural networks with long short-term memory are used as tools in the development of this protection system. The issues of software implementation of the proposed system and the formation of a data set containing network packets of a smart grid system are considered. The experimental results obtained using the generated data set demonstrated and confirmed the high efficiency of the proposed proactive smart grid protection system in detecting cyberattacks in real or near real-time, as well as in predicting the impact of cyberattacks and developing efficient measures to counter them. Full article
(This article belongs to the Special Issue Cybersecurity Issues in Smart Grids and Future Power Systems)
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25 pages, 7387 KiB  
Article
Cyber-Threat Detection System Using a Hybrid Approach of Transfer Learning and Multi-Model Image Representation
by Farhan Ullah, Shamsher Ullah, Muhammad Rashid Naeem, Leonardo Mostarda, Seungmin Rho and Xiaochun Cheng
Sensors 2022, 22(15), 5883; https://doi.org/10.3390/s22155883 - 6 Aug 2022
Cited by 15 | Viewed by 3273
Abstract
Currently, Android apps are easily targeted by malicious network traffic because of their constant network access. These threats have the potential to steal vital information and disrupt the commerce, social system, and banking markets. In this paper, we present a malware detection system [...] Read more.
Currently, Android apps are easily targeted by malicious network traffic because of their constant network access. These threats have the potential to steal vital information and disrupt the commerce, social system, and banking markets. In this paper, we present a malware detection system based on word2vec-based transfer learning and multi-model image representation. The proposed method combines the textual and texture features of network traffic to leverage the advantages of both types. Initially, the transfer learning method is used to extract trained vocab from network traffic. Then, the malware-to-image algorithm visualizes network bytes for visual analysis of data traffic. Next, the texture features are extracted from malware images using a combination of scale-invariant feature transforms (SIFTs) and oriented fast and rotated brief transforms (ORBs). Moreover, a convolutional neural network (CNN) is designed to extract deep features from a set of trained vocab and texture features. Finally, an ensemble model is designed to classify and detect malware based on the combination of textual and texture features. The proposed method is tested using two standard datasets, CIC-AAGM2017 and CICMalDroid 2020, which comprise a total of 10.2K malware and 3.2K benign samples. Furthermore, an explainable AI experiment is performed to interpret the proposed approach. Full article
(This article belongs to the Special Issue Cybersecurity Issues in Smart Grids and Future Power Systems)
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14 pages, 3775 KiB  
Article
Convergence Analysis of Path Planning of Multi-UAVs Using Max-Min Ant Colony Optimization Approach
by Muhammad Shafiq, Zain Anwar Ali, Amber Israr, Eman H. Alkhammash, Myriam Hadjouni and Jari Juhani Jussila
Sensors 2022, 22(14), 5395; https://doi.org/10.3390/s22145395 - 19 Jul 2022
Cited by 17 | Viewed by 2329
Abstract
Unmanned Aerial Vehicles (UAVs) seem to be the most efficient way of achieving the intended aerial tasks, according to recent improvements. Various researchers from across the world have studied a variety of UAV formations and path planning methodologies. However, when unexpected obstacles arise [...] Read more.
Unmanned Aerial Vehicles (UAVs) seem to be the most efficient way of achieving the intended aerial tasks, according to recent improvements. Various researchers from across the world have studied a variety of UAV formations and path planning methodologies. However, when unexpected obstacles arise during a collective flight, path planning might get complicated. The study needs to employ hybrid algorithms of bio-inspired computations to address path planning issues with more stability and speed. In this article, two hybrid models of Ant Colony Optimization were compared with respect to convergence time, i.e., the Max-Min Ant Colony Optimization approach in conjunction with the Differential Evolution and Cauchy mutation operators. Each algorithm was run on a UAV and traveled a predetermined path to evaluate its approach. In terms of the route taken and convergence time, the simulation results suggest that the MMACO-DE technique outperforms the MMACO-CM approach. Full article
(This article belongs to the Special Issue Cybersecurity Issues in Smart Grids and Future Power Systems)
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16 pages, 1087 KiB  
Article
A New Intrusion Detection System for the Internet of Things via Deep Convolutional Neural Network and Feature Engineering
by Safi Ullah, Jawad Ahmad, Muazzam A. Khan, Eman H. Alkhammash, Myriam Hadjouni, Yazeed Yasin Ghadi, Faisal Saeed and Nikolaos Pitropakis
Sensors 2022, 22(10), 3607; https://doi.org/10.3390/s22103607 - 10 May 2022
Cited by 34 | Viewed by 4216
Abstract
The Internet of Things (IoT) is a widely used technology in automated network systems across the world. The impact of the IoT on different industries has occurred in recent years. Many IoT nodes collect, store, and process personal data, which is an ideal [...] Read more.
The Internet of Things (IoT) is a widely used technology in automated network systems across the world. The impact of the IoT on different industries has occurred in recent years. Many IoT nodes collect, store, and process personal data, which is an ideal target for attackers. Several researchers have worked on this problem and have presented many intrusion detection systems (IDSs). The existing system has difficulties in improving performance and identifying subcategories of cyberattacks. This paper proposes a deep-convolutional-neural-network (DCNN)-based IDS. A DCNN consists of two convolutional layers and three fully connected dense layers. The proposed model aims to improve performance and reduce computational power. Experiments were conducted utilizing the IoTID20 dataset. The performance analysis of the proposed model was carried out with several metrics, such as accuracy, precision, recall, and F1-score. A number of optimization techniques were applied to the proposed model in which Adam, AdaMax, and Nadam performance was optimum. In addition, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. All experimental analysis indicates that the accuracy of the proposed approach is high and more robust than existing DL-based algorithms. Full article
(This article belongs to the Special Issue Cybersecurity Issues in Smart Grids and Future Power Systems)
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19 pages, 4293 KiB  
Article
Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning
by Mohammed Alsaedi, Fuad A. Ghaleb, Faisal Saeed, Jawad Ahmad and Mohammed Alasli
Sensors 2022, 22(9), 3373; https://doi.org/10.3390/s22093373 - 28 Apr 2022
Cited by 40 | Viewed by 6338
Abstract
Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are either vulnerable to web defacement attacks or [...] Read more.
Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are either vulnerable to web defacement attacks or created and managed by hackers such as fraudulent and phishing websites. Detecting malicious websites is essential to prevent the spreading of malware and protect end-users from being victims. However, most existing solutions rely on extracting features from the website’s content which can be harmful to the detection machines themselves and subject to obfuscations. Detecting malicious Uniform Resource Locators (URLs) is safer and more efficient than content analysis. However, the detection of malicious URLs is still not well addressed due to insufficient features and inaccurate classification. This study aims at improving the detection accuracy of malicious URL detection by designing and developing a cyber threat intelligence-based malicious URL detection model using two-stage ensemble learning. The cyber threat intelligence-based features are extracted from web searches to improve detection accuracy. Cybersecurity analysts and users reports around the globe can provide important information regarding malicious websites. Therefore, cyber threat intelligence-based (CTI) features extracted from Google searches and Whois websites are used to improve detection performance. The study also proposed a two-stage ensemble learning model that combines the random forest (RF) algorithm for preclassification with multilayer perceptron (MLP) for final decision making. The trained MLP classifier has replaced the majority voting scheme of the three trained random forest classifiers for decision making. The probabilistic output of the weak classifiers of the random forest was aggregated and used as input for the MLP classifier for adequate classification. Results show that the extracted CTI-based features with the two-stage classification outperform other studies’ detection models. The proposed CTI-based detection model achieved a 7.8% accuracy improvement and 6.7% reduction in false-positive rates compared with the traditional URL-based model. Full article
(This article belongs to the Special Issue Cybersecurity Issues in Smart Grids and Future Power Systems)
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16 pages, 2302 KiB  
Article
An Efficient Framework for Securing the Smart City Communication Networks
by Faisal Abdulaziz Alfouzan, Kyounggon Kim and Nouf M. Alzahrani
Sensors 2022, 22(8), 3053; https://doi.org/10.3390/s22083053 - 15 Apr 2022
Cited by 7 | Viewed by 3065
Abstract
Recently, smart cities have increasingly been experiencing an evolution to improve the lifestyle of citizens and society. These emerge from the innovation of information and communication technologies (ICT) which are able to create a new economic and social opportunities. However, there are several [...] Read more.
Recently, smart cities have increasingly been experiencing an evolution to improve the lifestyle of citizens and society. These emerge from the innovation of information and communication technologies (ICT) which are able to create a new economic and social opportunities. However, there are several challenges regarding our security and expectation of privacy. People are already involved and interconnected by using smart phones and other appliances. In many cities, smart energy meters, smart devices, and security appliances have recently been standardized. Full connectivity between public venues, homes, cares, and some other social systems are on their way to be applied, which are known as Internet of Things. In this paper, we aim to enhance the performance of security in smart city communication networks by using a new framework and scheme that provide an authentication and high confidentiality of data. The smart city system can achieve mutual authentication and establish the shared session key schemes between smart meters and the control center in order to secure a two-way communication channel. In our extensive simulation, we investigated and evaluated the security performance of the smart city communication network with and without our proposed scheme in terms of throughput, latency, load, and traffic received packet per seconds. Furthermore, we implemented and applied a man-in-the-middle (MITM) attack and network intrusion detection system (NIDS) in our proposed technique to validate and measure the security requirements maintaining the constrained resources. Full article
(This article belongs to the Special Issue Cybersecurity Issues in Smart Grids and Future Power Systems)
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Review

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26 pages, 816 KiB  
Review
Impact, Vulnerabilities, and Mitigation Strategies for Cyber-Secure Critical Infrastructure
by Hugo Riggs, Shahid Tufail, Imtiaz Parvez, Mohd Tariq, Mohammed Aquib Khan, Asham Amir, Kedari Vineetha Vuda and Arif I. Sarwat
Sensors 2023, 23(8), 4060; https://doi.org/10.3390/s23084060 - 17 Apr 2023
Cited by 13 | Viewed by 15410
Abstract
Several critical infrastructures are integrating information technology into their operations, and as a result, the cyber attack surface extends over a broad range of these infrastructures. Cyber attacks have been a serious problem for industries since the early 2000s, causing significant interruptions to [...] Read more.
Several critical infrastructures are integrating information technology into their operations, and as a result, the cyber attack surface extends over a broad range of these infrastructures. Cyber attacks have been a serious problem for industries since the early 2000s, causing significant interruptions to their ability to produce goods or offer services to their clients. The thriving cybercrime economy encompasses money laundering, black markets, and attacks on cyber-physical systems that result in service disruptions. Furthermore, extensive data breaches have compromised the personally identifiable information of millions of people. This paper aims to summarize some of the major cyber attacks that have occurred in the past 20 years against critical infrastructures. These data are gathered in order to analyze the types of cyber attacks, their consequences, vulnerabilities, as well as the victims and attackers. Cybersecurity standards and tools are tabulated in this paper in order to address this issue. This paper also provides an estimate of the number of major cyber attacks that will occur on critical infrastructure in the future. This estimate predicts a significant increase in such incidents worldwide over the next five years. Based on the study’s findings, it is estimated that over the next 5 years, 1100 major cyber attacks will occur on critical infrastructures worldwide, each causing more than USD 1 million in damages. Full article
(This article belongs to the Special Issue Cybersecurity Issues in Smart Grids and Future Power Systems)
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17 pages, 390 KiB  
Review
Augmented Reality (AR) and Cyber-Security for Smart Cities—A Systematic Literature Review
by Nouf M. Alzahrani and Faisal Abdulaziz Alfouzan
Sensors 2022, 22(7), 2792; https://doi.org/10.3390/s22072792 - 6 Apr 2022
Cited by 27 | Viewed by 8052
Abstract
Augmented Reality (AR) and cyber-security technologies have existed for several decades, but their growth and progress in recent years have increased exponentially. The areas of application for these technologies are clearly heterogeneous, most especially in purchase and sales, production, tourism, education, as well [...] Read more.
Augmented Reality (AR) and cyber-security technologies have existed for several decades, but their growth and progress in recent years have increased exponentially. The areas of application for these technologies are clearly heterogeneous, most especially in purchase and sales, production, tourism, education, as well as social interaction (games, entertainment, communication). Essentially, these technologies are recognized worldwide as some of the pillars of the new industrial revolution envisaged by the industry 4.0 international program, and are some of the leading technologies of the 21st century. The ability to provide users with required information about processes or procedures directly into the virtual environment is archetypally the fundamental factor in considering AR as an effective tool for different fields. However, the advancement in ICT has also brought about a variety of cybersecurity challenges, with a depth of evidence anticipating policy, architectural, design, and technical solutions in this very domain. The specific applications of AR and cybersecurity technologies have been described in detail in a variety of papers, which demonstrate their potential in diverse fields. In the context of smart cities, however, there is a dearth of sources describing their varied uses. Notably, a scholarly paper that consolidates research on AR and cybersecurity application in this context is markedly lacking. Therefore, this systematic review was designed to identify, describe, and synthesize research findings on the application of AR and cybersecurity for smart cities. The review study involves filtering information of their application in this setting from three key databases to answer the predefined research question. The keynote part of this paper provides an in-depth review of some of the most recent AR and cybersecurity applications for smart cities, emphasizing potential benefits, limitations, as well as open issues which could represent new challenges for the future. The main finding that we found is that there are five main categories of these applications for smart cities, which can be classified according to the main articles, such as tourism, monitoring, system management, education, and mobility. Compared with the general literature on smart cities, tourism, monitoring, and maintenance AR applications appear to attract more scholarly attention. Full article
(This article belongs to the Special Issue Cybersecurity Issues in Smart Grids and Future Power Systems)
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