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Applied Sciences
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Advances in Cyber Security
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Advances in Cyber Security

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 October 2025 | Viewed by 2056

Special Issue Editors

Dr. Georgi R. Tsochev

E-Mail Website
Guest Editor
Faculty of Computer Systems and Technologies, Technical University of Sofia, Sofia, Bulgaria
Interests: network security; information security; cyber security; computer networks
Dr. Maria Nenova

E-Mail Website
Guest Editor
Faculty of Telecomunications, Technical University of Sofia, Sofia, Bulgaria
Interests: reliability technique and systems; standards, protocols, analysis and implementation of security protection models against unauthorized access; reliability and security in communications; cyber security
Dr. Peican Zhu

E-Mail Website
Guest Editor
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University (NPU), Xi'an 710072, China
Interests: deep learning; artifical intelligent security; complex network; multi-modal data analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ki-Hyun Jung

E-Mail Website
Guest Editor
1. Department of Software Convergence, Gyeongkuk National University, 1375 Gyeongdong-ro, Andong-si 36729, Gyeongsangbuk-do, Republic of Korea
2. Department of Software Convergence, Andong National University, 1375 Gyeongdong-ro, Andong-si 36729, Gyeongsangbuk-do, Republic of Korea
Interests: information hiding; image security; visual symmetry; hybrid steganography; right-most digit replacement
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing sophistication of cyberattacks presents major challenges to individuals, businesses, and governments. As digital threats continue to evolve, there is an urgent need for innovative cybersecurity solutions to protect critical infrastructure and sensitive data. This Special Issue focuses on cutting-edge advancements in cybersecurity, aiming to explore new research, technologies, and strategies that enhance digital security and resilience.

We invite submissions that address emerging cybersecurity challenges across a range of topics, including, but not limited to, advanced threat detection, cyber defense strategies, secure communications, cryptography, digital forensics, and the role of artificial intelligence and machine learning in cybersecurity. Contributions focused on securing Internet of Things (IoT) devices and cloud infrastructure, as well as combating cybercrime, are also highly encouraged.

This Special Issue seeks original research articles, case studies, and experimental work that present innovative solutions to real-world cybersecurity problems. All submissions will undergo a thorough peer review process, ensuring the highest standards of quality and relevance. We look forward to contributions that push the boundaries of cybersecurity and offer practical solutions to the current challenges in the digital age.

Dr. Georgi R. Tsochev
Dr. Maria Nenova
Dr. Peican Zhu
Prof. Dr. Ki-Hyun Jung
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. Applied Sciences 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 2400 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
  • threat detection
  • cryptography
  • digital forensics
  • cyber defense
  • artificial intelligence in security
  • Internet of Things (IoT) security
  • cloud security
  • cybercrime
  • data privacy

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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29 pages, 669 KiB  
Article
LLM-Based Cyberattack Detection Using Network Flow Statistics
by Leopoldo Gutiérrez-Galeano, Juan-José Domínguez-Jiménez, Jörg Schäfer and Inmaculada Medina-Bulo
Appl. Sci. 2025, 15(12), 6529; https://doi.org/10.3390/app15126529 - 10 Jun 2025
Viewed by 133
Abstract
Cybersecurity is a growing area of research due to the constantly emerging new types of cyberthreats. Tools and techniques exist to keep systems secure against certain known types of cyberattacks, but are insufficient for others that have recently appeared. Therefore, research is needed [...] Read more.
Cybersecurity is a growing area of research due to the constantly emerging new types of cyberthreats. Tools and techniques exist to keep systems secure against certain known types of cyberattacks, but are insufficient for others that have recently appeared. Therefore, research is needed to design new strategies to deal with new types of cyberattacks as they arise. Existing tools that harness artificial intelligence techniques mainly use artificial neural networks designed from scratch. In this paper, we present a novel approach for cyberattack detection using an encoder–decoder pre-trained Large Language Model (T5), fine-tuned to adapt its classification scheme for the detection of cyberattacks. Our system is anomaly-based and takes statistics of already finished network flows as input. This work makes significant contributions by introducing a novel methodology for adapting its original task from natural language processing to cybersecurity, achieved by transforming numerical network flow features into a unique abstract artificial language for the model input. We validated the robustness of our detection system across three datasets using undersampling. Our model achieved consistently high performance across all evaluated datasets. Specifically, for the CIC-IDS-2017 dataset, we obtained an accuracy, precision, recall, and F-score of more than 99.94%. For CSE-CIC-IDS-2018, these metrics exceeded 99.84%, and for BCCC-CIC-IDS-2017, they were all above 99.90%. These results collectively demonstrate superior performance for cyberattack detection, while maintaining highly competitive false-positive rates and false-negative rates. This efficacy is achieved by relying exclusively on real-world network flow statistics, without the need for synthetic data generation. Full article
(This article belongs to the Special Issue Advances in Cyber Security)
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20 pages, 5099 KiB  
Article
Optimizing Activation Function for Parameter Reduction in CNNs on CIFAR-10 and CINIC-10
by Vasil Vasilev, Vasil Shterev and Maria Nenova
Appl. Sci. 2025, 15(8), 4292; https://doi.org/10.3390/app15084292 - 13 Apr 2025
Viewed by 1071
Abstract
This paper explores a simple CNN architecture used for image classification. Since the first introduction of the CNN idea, LeNet5, CNNs have become the main method for image exploration. A previously unexplored activation function is proposed, which improves the accuracy on one hand, [...] Read more.
This paper explores a simple CNN architecture used for image classification. Since the first introduction of the CNN idea, LeNet5, CNNs have become the main method for image exploration. A previously unexplored activation function is proposed, which improves the accuracy on one hand, while reducing the execution time. The other positive side is the faster convergence of the loss function. Unlike other well-known activation functions, such as ReLU, Tanh, and others, Exponential Partial Unit (EPU) does not overfit after 100 or more iterations. For this study, the CIFAR-10 dataset is chosen, which is a benchmark for this kind of research. This paper aims to present another view on CNNs, showing that effective networks can be trained with fewer parameters and reduced computational resources. Full article
(This article belongs to the Special Issue Advances in Cyber Security)
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Other

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25 pages, 1047 KiB  
Perspective
Evaluating Cybersecurity Risks of Bulgaria’s Energy Sector: Focus on PV and HVAC-R
by Vultchan Gueorgiev, Valentin Totev, Plamen Tsankov and Stoyan Stoyanov
Appl. Sci. 2025, 15(12), 6672; https://doi.org/10.3390/app15126672 (registering DOI) - 13 Jun 2025
Abstract
Photovoltaics with energy storage are the current trend in solar energy. Hybrid inverters are the backbone of low-power installations of this type. If a single installation is compromised, there are no significant security concerns. However, multiple devices can be targeted simultaneously. Taking into [...] Read more.
Photovoltaics with energy storage are the current trend in solar energy. Hybrid inverters are the backbone of low-power installations of this type. If a single installation is compromised, there are no significant security concerns. However, multiple devices can be targeted simultaneously. Taking into account their increasing share in the energy mix, distributed cyber-attacks against these devices can threaten grid stability. The Bulgarian electric power system has been analyzed in order to determine its development which is in line with EU-wide trends. It can be concluded that hybrid inverters are expected to grow rapidly in number and in installed power. The vulnerability of hybrid inverters to cyber-attacks has been analyzed, and the possible consequences for the energy system have been identified. The technology allows it to be used as a hybrid means of influence, and this aspect is poorly addressed in existing cybersecurity regulations. A risk assessment has been made, based on which measures to improve security have been proposed. Full article
(This article belongs to the Special Issue Advances in Cyber Security)
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