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Energies
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Research on Security and Data Protection for Energy Systems
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Research on Security and Data Protection for Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (10 September 2024) | Viewed by 5593

Special Issue Editors

Prof. Dr. Marcin Niemiec

E-Mail Website
Guest Editor
Department of Telecommunications, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland
Interests: cybersecurity; security services; symmetric ciphers; intrusion detection; malware analysis; risk management; quantum cryptography
Special Issues, Collections and Topics in MDPI journals
Dr. Robert Ryszard Chodorek

E-Mail Website
Guest Editor
Department of Telecommunications, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland
Interests: cybersecurity; Internet of Things; unmanned aerial vehicles; broadband communications; IP networking; multimedia communications; cloud computing

Special Issue Information

Dear Colleagues,

The ongoing evolution of information technology (IT) continues to pose new challenges, including for energy systems. As telecommunication networks become an integral part of today’s world, new threats are emerging. Therefore, the continuous development of cybersecurity solutions is needed. The rapid development of cryptography techniques, machine learning, quantum technology, and other solutions has helped us to develop innovative security methods and algorithms.  As such, this Special Issue mainly focuses on recent advances in cybersecurity for effective data protection in energy systems to ensure a high level of cybersecurity and to prevent cyberthreats.

We invite both theoretical and experimental studies in the field of cybersecurity solutions for energy systems. The proposed papers should consist of novel and original ideas and results. Topics of interest include, but are not limited to:

  • Cybersecurity solutions of energy systems;
  • Security of smart grids;
  • Cryptography techniques and protocols;
  • Network security solutions;
  • Data and privacy protection;
  • Security services using emerging technologies, e.g., blockchain, etc.;
  • Security services in emerging technologies, e.g., Internet of Things (IoT), Industry 4.0, unmanned vehicles, etc.;
  • Detection and mitigation of cyberattacks;
  • Technology challenges and opportunities in cybersecurity.

Prof. Dr. Marcin Niemiec
Dr. Robert Ryszard Chodorek
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

  • cybersecurity of energy systems
  • smart grids
  • machine learning for cybersecurity
  • cyberattacks and intrusion detection methods
  • Internet of Things
  • unmanned vehicles
  • cryptography
  • blockchain
  • threat intelligence
  • cybersecurity challenges and opportunities

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Published Papers (3 papers)

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Research

26 pages, 2854 KiB  
Article
Federated Deep Learning Model for False Data Injection Attack Detection in Cyber Physical Power Systems
by Firdous Kausar, Sambrdhi Deo, Sajid Hussain and Zia Ul Haque
Energies 2024, 17(21), 5337; https://doi.org/10.3390/en17215337 - 26 Oct 2024
Viewed by 770
Abstract
Cyber-physical power systems (CPPS) integrate information and communication technology into conventional electric power systems to facilitate bidirectional communication of information and electric power between users and power grids. Despite its benefits, the open communication environment of CPPS is vulnerable to various security attacks. [...] Read more.
Cyber-physical power systems (CPPS) integrate information and communication technology into conventional electric power systems to facilitate bidirectional communication of information and electric power between users and power grids. Despite its benefits, the open communication environment of CPPS is vulnerable to various security attacks. This paper proposes a federated deep learning-based architecture to detect false data injection attacks (FDIAs) in CPPS. The proposed work offers a strong, decentralized alternative with the ability to boost detection accuracy while maintaining data privacy, presenting a significant opportunity for real-world applications in the smart grid. This framework combines state-of-the-art machine learning and deep learning models, which are used in both centralized and federated learning configurations, to boost the detection of false data injection attacks in cyber-physical power systems. In particular, the research uses a multi-stage detection framework that combines several models, including classic machine learning classifiers like Random Forest and ExtraTrees Classifiers, and deep learning architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The results demonstrate that Bidirectional GRU and LSTM models with attention layers in a federated learning setup achieve superior performance, with accuracy approaching 99.8%. This approach enhances both detection accuracy and data privacy, offering a robust solution for FDIA detection in real-world smart grid applications. Full article
(This article belongs to the Special Issue Research on Security and Data Protection for Energy Systems)
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23 pages, 360 KiB  
Article
Internet Threat Detection in Smart Grids Based on Network Traffic Analysis Using LSTM, IF, and SVM
by Szymon Stryczek and Marek Natkaniec
Energies 2023, 16(1), 329; https://doi.org/10.3390/en16010329 - 28 Dec 2022
Cited by 13 | Viewed by 2195
Abstract
The protection of users of ICT networks, including smart grids, is a challenge whose importance is constantly growing. Internet of Things (IoT) or Internet of Energy (IoE) devices, as well as network resources, store more and more information about users. Large institutions use [...] Read more.
The protection of users of ICT networks, including smart grids, is a challenge whose importance is constantly growing. Internet of Things (IoT) or Internet of Energy (IoE) devices, as well as network resources, store more and more information about users. Large institutions use extensive security systems requiring large and expensive resources. For smart grid users, this becomes difficult. Efficient methods are needed to take advantage of limited sets of traffic features. In this paper, machine learning techniques to verify network events for recognition of Internet threats were analyzed, intentionally using a limited number of parameters. The authors considered three machine learning techniques: Long Short-Term Memory, Isolation Forest, and Support Vector Machine. The analysis is based on two datasets. In the paper, the data preparation process is also described. Eight series of results were collected and compared with other studies. The results showed significant differences between the techniques, the size of the datasets, and the balance of the datasets. We also showed that a more accurate classification could be achieved by increasing the number of analyzed features. Unfortunately, each increase in the number of elements requires more extensive analysis. The work ends with a description of the steps that can be taken in the future to improve the operation of the models and enable the implementation of the described methods of analysis in practice. Full article
(This article belongs to the Special Issue Research on Security and Data Protection for Energy Systems)
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18 pages, 548 KiB  
Article
A Sponge-Based Key Expansion Scheme for Modern Block Ciphers
by Maciej Sawka and Marcin Niemiec
Energies 2022, 15(19), 6864; https://doi.org/10.3390/en15196864 - 20 Sep 2022
Cited by 3 | Viewed by 2314
Abstract
Many systems in use today require strong cryptographic primitives to ensure confidentiality and integrity of data. This is especially true for energy systems, such as smart grids, as their proper operation is crucial for the existence of a functioning society. Because of this, [...] Read more.
Many systems in use today require strong cryptographic primitives to ensure confidentiality and integrity of data. This is especially true for energy systems, such as smart grids, as their proper operation is crucial for the existence of a functioning society. Because of this, we observe new developments in the field of cryptography every year. Among the developed primitives, one of the most important and widely used are iterated block ciphers. From AES (Advanced Encryption Standard) to LEA (Lightweight Encryption Algorithm), these ciphers are omnipresent in our world. While security of the encryption process of these ciphers is often meticulously tested and verified, an important part of them is neglected—the key expansion. Many modern ciphers use key expansion algorithms which produce reversible sub-key sequences. This means that, if the attacker finds out a large-enough part of this sequence, he/she will be able to either calculate the rest of the sequence, or even the original key. This could completely compromise the cipher. This is especially concerning due to research done into side-channel attacks, which attempt to leak secret information from memory. In this paper, we propose a novel scheme which can be used to create key expansion algorithms for modern ciphers. We define two important properties that a sequence produced by such algorithm should have and ensure that our construction fulfills them, based on the research on hashing functions. In order to explain the scheme, we describe an example algorithm constructed this way, as well as a cipher called IJON which utilizes it. In addition to this, we provide results of statistical tests which show the unpredictability of the sub-key sequence produced this way. The tests were performed using a test suite standardized by NIST (National Institute for Standards and Technology). The methodology of our tests is also explained. Finally, the reference implementation of the IJON cipher is published, ready to be used in software. Based on the results of tests, we conclude that, while more research and more testing of the algorithm is advised, the proposed key expansion scheme provides a very good generation of unpredictable bits and could possibly be used in practice. Full article
(This article belongs to the Special Issue Research on Security and Data Protection for Energy Systems)
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