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

Deadline for manuscript submissions: 20 May 2025 | Viewed by 15197

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Special Issue Editor

Dr. Reiner Creutzburg

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Guest Editor

1. Department of Informatics and Media, Technische Hochschule Brandenburg, Magdeburger Str. 50, D-14770 Brandenburg, Germany
2. Berlin School of Technology, SRH Berlin Applied University of Technology, Ernst-Reuter-Platz 10, D-10587 Berlin, Germany
Interests: cybersecurity

Special Issue Information

Dear Colleagues,

Recent advances in cybersecurity have significantly enhanced protection mechanisms against cyber threats. This Special Issue in the MDPI Journal of Applied Sciences delves into cutting-edge research and innovative practices across various computer security and cybersecurity aspects. A key focus is enhancing cyber resilience, which aims to fortify systems against attacks and ensure swift recovery from breaches. Furthermore, open-source intelligence (OSINT) is now essential for preemptive threat detection and strategic security planning.

The protection of critical infrastructure is a top priority, as vulnerabilities in these systems can cause severe economic and social consequences. This Special Issue's articles present robust strategies for shielding such infrastructures from sophisticated cyber-attacks. Furthermore, the integration of cybersecurity measures within cyber–physical systems is highlighted, underscoring the importance of securing the interconnected elements that manage physical processes through computer-based algorithms.

The journal's convergence of these themes results in a comprehensive overview of current trends and future directions in the field of computer security. This collection of research provides deep insights into technological advancements and discusses the broader implications of these developments for society and policy-making.

Dr. Reiner Creutzburg
Guest Editor

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

cyber security
computer security
cyber resilience
open-source intelligence (OSINT)
critical infrastructure protection
cyber–physical systems
secure communications and cryptography

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Published Papers (10 papers)

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Research

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24 pages, 2927 KiB

Open AccessArticle

Text Mining Approaches for Exploring Research Trends in the Security Applications of Generative Artificial Intelligence

by Jinsick Kim, Byeongsoo Koo, Moonju Nam, Kukjin Jang, Jooyeoun Lee, Myoungsug Chung and Youngseo Song

Appl. Sci. 2025, 15(6), 3355; https://doi.org/10.3390/app15063355 - 19 Mar 2025

Viewed by 514

Abstract

This study examines the security implications of generative artificial intelligence (GAI), focusing on models such as ChatGPT. As GAI technologies are increasingly integrated into industries like healthcare, education, and media, concerns are growing regarding security vulnerabilities, ethical challenges, and potential for misuse. This study not only synthesizes existing research but also conducts an original scientometric analysis using text mining techniques. To address these concerns, this research analyzes 1047 peer-reviewed academic articles from the SCOPUS database using scientometric methods, including Term Frequency–Inverse Document Frequency (TF-IDF) analysis, keyword centrality analysis, and Latent Dirichlet Allocation (LDA) topic modeling. The results highlight significant contributions from countries such as the United States, China, and India, with leading institutions like the Chinese Academy of Sciences and the National University of Singapore driving research on GAI security. In the keyword centrality analysis, “ChatGPT” emerged as a highly central term, reflecting its prominence in the research discourse. However, despite its frequent mention, “ChatGPT” showed lower proximity centrality than terms like “model” and “AI”. This suggests that while ChatGPT is broadly associated with other key themes, it has a less direct connection to specific research subfields. Topic modeling identified six major themes, including AI and security in education, language models, data processing, and risk management. The analysis emphasizes the need for robust security frameworks to address technical vulnerabilities, ensure ethical responsibility, and manage risks in the safe deployment of AI systems. These frameworks must incorporate not only technical solutions but also ethical accountability, regulatory compliance, and continuous risk management. This study underscores the importance of interdisciplinary research that integrates technical, legal, and ethical perspectives to ensure the responsible and secure deployment of GAI technologies. Full article

(This article belongs to the Special Issue New Advances in Computer Security and Cybersecurity)

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33 pages, 3643 KiB

Open AccessArticle

A Flexible Risk-Based Security Evaluation Methodology for Information Communication Technology System Certification

by Sara N. Matheu, Juan F. Martínez-Gil, Irene Bicchierai, Jan Marchel, Radosław Piliszek and Antonio Skarmeta

Appl. Sci. 2025, 15(3), 1600; https://doi.org/10.3390/app15031600 - 5 Feb 2025

Viewed by 833

Abstract

As Information and Communication Technology (ICT) systems become increasingly complex, the need for adaptable and efficient security certification frameworks grows. This paper introduces a flexible security evaluation methodology designed to serve as the foundation for cybersecurity certification across diverse ICT systems. The proposed methodology integrates risk assessment and test-based evaluation, offering a scalable approach that adapts to different tools and processes, addressing the limitations of existing rigid certification schemes. The certification approach expands on ETSI’s Risk-Based Security Assessment and Testing methods, based on ISO 31000 and ISO 29119, and it integrates widely recognized standards such as MUD. This ensures an objective, empirical evaluation process that enables partial automation and simplifies recertification. As a proof of concept, we validate the methodology in two real use cases, an ICT gateway for smart grids and an AI-powered investments platform, demonstrating its flexibility and applicability to real-world contexts while addressing the challenges of modern ICT ecosystems. Full article

(This article belongs to the Special Issue New Advances in Computer Security and Cybersecurity)

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31 pages, 4117 KiB

Open AccessArticle

A Decentralized Storage and Security Engine (DeSSE) Using Information Fusion Based on Stochastic Processes and Quantum Mechanics

by Gerardo Iovane and Riccardo Amatore

Appl. Sci. 2025, 15(2), 759; https://doi.org/10.3390/app15020759 - 14 Jan 2025

Cited by 1 | Viewed by 1087

Abstract

In the context of data security, this work aims to present a novel solution that, rather than addressing the topic of endpoint security—which has already garnered significant attention within the international scientific community—offers a different perspective on the subject. In other words, the focus is not on device security but rather on the protection and security of the information contained within those devices. As we will see, the result is a next-generation decentralized infrastructure that simultaneously integrates two cognitive areas: data storage and its protection and security. In this context, an innovative Multiscale Relativistic Quantum (MuReQua) chain is considered to realize a novel decentralized and security solution for storing data. This engine is based on the principles of Quantum Mechanics, stochastic processes, and a new approach of decentralization for data storage focused on information security. The solution is broken down into four main components, considered four levels of security against attackers: (i) defocusing, (ii) fogging, (iii) puzzling, and (iv) crypto agility. The defocusing is realized thanks to a fragmentation of the contents and their distributions on different allocations, while the fogging is a component consisting of a solution of hybrid cyphering. Then, the puzzling is a unit of Information Fusion and Inverse Information Fusion, while the crypto agility component is a frontier component based on Quantum Computing, which gives a stochastic dynamic to the information and, in particular, to its data fragments. The data analytics show a very effective and robust solution, with executions time comparable with cloud technologies, but with a level of security that is a post quantum one. In the end, thanks to a specific application example, going beyond purely technical and technological aspects, this work introduces a new cognitive perspective regarding (i) the distinction between data and information, and (ii) the differentiation between the owner and the custodian of data. Full article

(This article belongs to the Special Issue New Advances in Computer Security and Cybersecurity)

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21 pages, 8009 KiB

Open AccessArticle

Explainable AI for DeepFake Detection

by Nazneen Mansoor and Alexander I. Iliev

Appl. Sci. 2025, 15(2), 725; https://doi.org/10.3390/app15020725 - 13 Jan 2025

Viewed by 3127

Abstract

The surge in technological advancements has resulted in concerns over its misuse in politics and entertainment, making reliable detection methods essential. This study introduces a deepfake detection technique that enhances interpretability using the network dissection algorithm. This research consists of two stages: (1) detection of forged images using advanced convolutional neural networks such as ResNet-50, Inception V3, and VGG-16, and (2) applying the network dissection algorithm to understand the models’ internal decision-making processes. The CNNs’ performance is evaluated through F1-scores ranging from 0.8 to 0.9, demonstrating their effectiveness. By analyzing the facial features learned by the models, this study provides explainable results for classifying images as real or fake. This interpretability is crucial in understanding how deepfake detection models operate. Although numerous detection models exist, they often lack transparency in their decision-making processes. This research fills that gap by offering insights into how these models distinguish real from manipulated images. The findings highlight the importance of interpretability in deep neural networks, providing a better understanding of their hierarchical structures and decision processes. Full article

(This article belongs to the Special Issue New Advances in Computer Security and Cybersecurity)

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29 pages, 4662 KiB

Open AccessArticle

A Country Risk Assessment from the Perspective of Cybersecurity in Local Entities

by Javier Sanchez-Zurdo and Jose San-Martín

Appl. Sci. 2024, 14(24), 12036; https://doi.org/10.3390/app142412036 - 23 Dec 2024

Cited by 1 | Viewed by 928

Abstract

The number of vulnerabilities identified annually has increased substantially, thereby raising the risks associated with online services. The implementation of cybersecurity management measures in accordance with the European NIS2 Directive is optional at the local authority level. This study analyzes the external perimeter of nearly 7000 municipalities and proposes a simplified security framework that provides a comprehensive view of security across regions. A complete data set was assembled on the Technological and Competence profiles of all municipalities in Spain over a two-year period. The data were gathered from the external perimeter in relation to security, availability and SEO posture areas. A survey was conducted to determine the level of concern among citizens regarding cybersecurity issues in online municipal services, with 188 respondents. Some regions were identified as exhibiting particularly high and homogeneous levels of security. In contrast, other regions were found to be below the expected level. The presence of supra-local entities, such as the “Diputaciones”, has been demonstrated to facilitate the harmonization of regional security, while simultaneously reducing technological fragmentation and operational expenditure. Full article

(This article belongs to the Special Issue New Advances in Computer Security and Cybersecurity)

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32 pages, 1395 KiB

Open AccessArticle

Addressing Cybersecurity Challenges in Times of Crisis: Extending the Sociotechnical Systems Perspective

by Samreen Mahmood, Mehmood Chadhar and Selena Firmin

Appl. Sci. 2024, 14(24), 11610; https://doi.org/10.3390/app142411610 - 12 Dec 2024

Viewed by 2190

Abstract

Recent crises have significantly amplified cybersecurity challenges. Numerous studies have identified these challenges during major crises; however, empirical investigations using a sociotechnical systems (STS) theoretical perspective remain limited. Against this backdrop, this research study examines and categorizes cybersecurity challenges in the Higher Education and Research Sector (HERS) through the lens of STS theory. Utilizing a qualitative methodology, semi-structured interviews were conducted with cybersecurity experts and top managers. This study proposes an STS cybersecurity framework, classifying challenges into five subsystems: social, technical, political, economic, and environmental. This framework expands on previous literature by incorporating factors often overlooked, such as cybersecurity challenges arising from internal and external environmental conditions, legal and regulatory political factors, and national and global economic factors. This research provides valuable theoretical and practical insights applicable beyond the context of the recent crisis. Full article

(This article belongs to the Special Issue New Advances in Computer Security and Cybersecurity)

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29 pages, 2233 KiB

Open AccessArticle

AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting

by Klaus Schwarz, Kendrick Bollens, Daniel Arias Aranda and Michael Hartmann

Appl. Sci. 2024, 14(23), 11165; https://doi.org/10.3390/app142311165 - 29 Nov 2024

Viewed by 1047

Abstract

This paper presents the Open-Source Intelligence Disaster Event Tracker (ODET), a modular platform that provides customizable endpoints and agents for each processing step. ODET enables the implementation of AI-enhanced algorithms to respond to various complex disaster scenarios. To evaluate ODET, we conducted two case studies using unmodified AI models to demonstrate its base performance and potential applications. Through our case studies on Hurricane Harvey and the 2023 Turkey earthquake, we show how complex tasks can be quickly broken down with ODET while achieving a score of up to 89% using the AlignScore metric. ODET enables compliance with Berkeley protocol requirements by ensuring data privacy and using privacy-preserving processing methods. Our results demonstrate that ODET is a robust platform for the long-term monitoring and analysis of dynamic environments and can improve the efficiency and accuracy of situational awareness reports in disaster management. Full article

(This article belongs to the Special Issue New Advances in Computer Security and Cybersecurity)

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21 pages, 526 KiB

Open AccessArticle

Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET

by Rudyard Fuster, Patricio Galdames and Claudio Gutierréz-Soto

Appl. Sci. 2024, 14(22), 10480; https://doi.org/10.3390/app142210480 - 14 Nov 2024

Viewed by 776

Abstract

This paper addresses the challenge of preserving user privacy in location-based services (LBSs) by proposing a novel, complementary approach to existing privacy-preserving techniques such as k-anonymity and l-diversity. Our approach implements collaborative caching strategies within a mobile ad hoc network (MANET), exploiting the geographic of location-based queries (LBQs) to reduce data exposure to untrusted LBS servers. Unlike existing approaches that rely on centralized servers or stationary infrastructure, our solution facilitates direct data exchange between users’ devices, providing an additional layer of privacy protection. We introduce a new privacy entropy-based metric called accumulated privacy loss (APL) to quantify the privacy loss incurred when accessing either the LBS or our proposed system. Our approach implements a two-tier caching strategy: local caching maintained by each user and neighbor caching based on proximity. This strategy not only reduces the number of queries to the LBS server but also significantly enhances user privacy by minimizing the exposure of location data to centralized entities. Empirical results demonstrate that while our collaborative caching system incurs some communication costs, it significantly mitigates redundant data among user caches and reduces the need to access potentially privacy-compromising LBS servers. Our findings show a 40% reduction in LBS queries, a 64% decrease in data redundancy within cells, and a 31% reduction in accumulated privacy loss compared to baseline methods. In addition, we analyze the impact of data obsolescence on cache performance and privacy loss, proposing mechanisms for maintaining the relevance and accuracy of cached data. This work contributes to the field of privacy-preserving LBSs by providing a decentralized, user-centric approach that improves both cache redundancy and privacy protection, particularly in scenarios where central infrastructure is unreachable or untrusted. Full article

(This article belongs to the Special Issue New Advances in Computer Security and Cybersecurity)

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13 pages, 1300 KiB

Open AccessArticle

PEDRO: Privacy-Enhancing Decision suppoRt tOol

by Paul van Schaik and Karen Renaud

Appl. Sci. 2024, 14(20), 9275; https://doi.org/10.3390/app14209275 - 11 Oct 2024

Viewed by 1022

Abstract

Citizens face online privacy threats from social media, online service providers and governments. Privacy-enhancing tools (PETs) can prevent privacy invasion, but the uptake of these is limited. We developed a novel conceptual framework for privacy self-protection, consisting of a classification framework of four distinct privacy threats and our own novel staged model of PET adoption requisites. Through an expert survey (N = 12) and a lay user survey (N = 500), we identified suitable PETs for non-expert users and identified potential barriers to PET adoption. Based on the studies and our theoretical framework, we then developed and implemented a PET decision support tool called PEDRO, and conducted expert evaluations (N = 10) to confirm the validity of its recommendations. Full article

(This article belongs to the Special Issue New Advances in Computer Security and Cybersecurity)

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31 pages, 920 KiB

Open AccessSystematic Review

Risk Assessment for Cyber Resilience of Critical Infrastructures: Methods, Governance, and Standards

by Ali Aghazadeh Ardebili, Marianna Lezzi and Mahdad Pourmadadkar

Appl. Sci. 2024, 14(24), 11807; https://doi.org/10.3390/app142411807 - 17 Dec 2024

Viewed by 2300

Abstract

As future infrastructures increasingly rely on digital systems, their exposure to cyber threats has grown significantly. The complex and hyper-connected nature of these systems presents challenges for enhancing cyber resilience against adverse conditions, stresses, attacks, or compromises on cybersecurity resources. Integrating risk assessment with cyber resilience allows for adaptive approaches that can effectively safeguard critical infrastructures (CIs) against evolving cyber risks. However, the wide range of methods, frameworks, and standards—some overlapping and others inadequately addressed in the literature—complicates the selection of an appropriate approach to cyber risk assessment for cyber resilience. To investigate this integration, this study conducts a systematic literature review (SLR) of relevant methodologies, standards, and regulations. After conducting the initial screening of 173 publications on risk assessment and cyber resilience, 40 papers were included for thorough review. The findings highlight risk assessment methods, standards, and guidelines used for cyber resilience and provide an overview of relevant regulations that strengthen cyber resilience through risk assessment practices. The results of this paper will offer cybersecurity researchers and decision-makers an illuminated understanding of how risk assessment enhances cyber resilience by extracting risk assessment best practices in the literature supported by relevant standards and regulations. Full article

(This article belongs to the Special Issue New Advances in Computer Security and Cybersecurity)

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