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Advanced Technologies in Data and Information Security, Fourth Edition

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 March 2025 | Viewed by 1578

Special Issue Editors


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Guest Editor
Institute for Language and Speech Processing, Athena Research Centre, Kimmeria University Campus, 67100 Xanthi, Greece
Interests: privacy-enhancing technologies (PETs); information security; distributed ledger technologies (DLTs); personal data management; cryptographic protocols; health informatics; information retrieval; social networks analysis; ubiquitous computing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Science, Democritus University of Thrace, 65404 Kavala, Greece
Interests: cybersecurity; IoT security; cyber threat intelligence; authentication systems; e-Government services; electronic payment systems; mobile systems security; security awareness
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The protection of personal data and privacy is a timeless challenge that has intensified in the modern era. The digitisation that has been achieved in recent decades has radically changed the way we live, communicate, and work, revealing various security and privacy issues. Specifically, the explosion of new technologies and the continuous developments of technologies, such as IoT and AI, have led to the increased value of data, while it has raised demand and introduced new ways to obtain it. Techniques such as data analysis and processing provide a set of powerful tools that can be used by both governments and businesses for specific purposes. However, as with any valuable resource, as in the case of data, the phenomena of abuse, unfair practises, and even criminal acts are not absent. In particular, in recent years, there have been more and more cases of sophisticated cyberattacks, data theft and leaks, or even data trade, which violate the rights of individuals, but also harm competition and seriously damage the reputation of businesses.

In this Special Issue, we seek research and case studies that demonstrate the application of advanced technologies in data and information security to support applied scientific research, in any area of science and technology. Example topics include (but are not limited to) the following:

  1. Self-sovereign identities;
  2. Privacy-preserving solutions;
  3. Blockchain-based security and privacy;
  4. Data loss prevention;
  5. Deep learning forensics/malware analysis/anomaly detection;
  6. AI-driven security systems;
  7. Context-aware behavioural analytics;
  8. Security and data breach detection;
  9. Cyber-physical systems security;
  10. Secure and privacy-preserving health solutions;
  11. Active defence measures;
  12. Social networks information leaks;
  13. Edge and fog computing security;
  14. Anonymization and pseudonymization solutions;
  15. Zero-trust network access technology;
  16. Dynamic risk management;
  17. Cyber threat intelligence;
  18. Situational awareness.

Dr. George Drosatos
Dr. Konstantinos Rantos
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

  • data protection
  • information security
  • cybersecurity
  • cyber threats
  • privacy
  • forensics
  • cryptography
  • blockchain
  • AI- and ML- driven security

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Related Special Issues

Published Papers (4 papers)

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Research

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18 pages, 2639 KiB  
Article
Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
by Mingxu Yang, Chuhua Huang, Xin Huang and Shengjin Hou
Appl. Sci. 2025, 15(5), 2583; https://doi.org/10.3390/app15052583 - 27 Feb 2025
Viewed by 117
Abstract
Edge-assisted visual simultaneous localization and mapping (SLAM) is widely used in autonomous driving, robot navigation, and augmented reality for environmental perception, map construction, and real-time positioning. However, it poses significant privacy risks, as input images may contain sensitive information, and generated 3D point [...] Read more.
Edge-assisted visual simultaneous localization and mapping (SLAM) is widely used in autonomous driving, robot navigation, and augmented reality for environmental perception, map construction, and real-time positioning. However, it poses significant privacy risks, as input images may contain sensitive information, and generated 3D point clouds can reconstruct original scenes. To address these concerns, this paper proposes a dual-component privacy-preserving approach for visual SLAM. First, a privacy protection method for images is proposed, which combines object detection and image inpainting to protect privacy-sensitive information in images. Second, an encryption algorithm is introduced to convert 3D point cloud data into a 3D line cloud through dimensionality enhancement. Integrated with ORB-SLAM3, the proposed method is evaluated on the Oxford Robotcar and KITTI datasets. Results demonstrate that it effectively safeguards privacy-sensitive information while ORB-SLAM3 maintains accurate pose estimation in dynamic outdoor scenes. Furthermore, the encrypted line cloud prevents unauthorized attacks on recovering the original point cloud. This approach enhances privacy protection in visual SLAM and is expected to expand its potential applications. Full article
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28 pages, 432 KiB  
Article
A Dynamic Risk Assessment and Mitigation Model
by Pavlos Cheimonidis and Konstantinos Rantos
Appl. Sci. 2025, 15(4), 2171; https://doi.org/10.3390/app15042171 - 18 Feb 2025
Viewed by 302
Abstract
In the current operational landscape, organizations face a growing and diverse array of cybersecurity challenges, necessitating the development and implementation of innovative and effective security solutions. This paper presents a novel methodology for dynamic risk assessment and mitigation suggestions aimed at assessing and [...] Read more.
In the current operational landscape, organizations face a growing and diverse array of cybersecurity challenges, necessitating the development and implementation of innovative and effective security solutions. This paper presents a novel methodology for dynamic risk assessment and mitigation suggestions aimed at assessing and reducing cyber risks. The proposed approach gathers information from publicly available cybersecurity-related open sources and integrates it with environment-specific data to generate a comprehensive understanding of potential risks. It creates multiple distinct risk scenarios based on the identification of vulnerabilities, network topology, and the attacker’s perspective. The methodology employs Bayesian networks to proactively and dynamically estimate the probability of threats and Fuzzy Cognitive Maps to dynamically update vulnerability severity values for each risk scenario. These elements are combined with impact estimations to provide dynamic risk assessments. Furthermore, the methodology offers mitigation suggestions for each identified vulnerability across all risk scenarios, enabling organizations to effectively address the assessed cybersecurity risks. To validate the effectiveness of the proposed methodology, a case study is presented, demonstrating its practical application and efficacy. Full article
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14 pages, 3053 KiB  
Article
Cyber Environment Test Framework for Simulating Command and Control Attack Methods with Reinforcement Learning
by Minki Jeong, Jongyoul Park and Sang Ho Oh
Appl. Sci. 2025, 15(4), 2120; https://doi.org/10.3390/app15042120 - 17 Feb 2025
Viewed by 293
Abstract
Recently, the IT industry has become larger, and cloud service has rapidly increased; thus cybersecurity to protect sensitive data from attacks has become an important factor. However, cloud services have become larger, making the surface area larger, and a complex cyber environment leads [...] Read more.
Recently, the IT industry has become larger, and cloud service has rapidly increased; thus cybersecurity to protect sensitive data from attacks has become an important factor. However, cloud services have become larger, making the surface area larger, and a complex cyber environment leads to difficulty managing and defending. With the rise of artificial intelligence, applying artificial intelligence to a cyber environment to automatically detect and respond to cyberattacks has begun to get attention. In order to apply artificial intelligence in cyber environments, a simulation framework that is easily applicable and can represent real situations well is needed. In this study, we introduce the framework Cyber Environment (CYE) that provides useful components that abstract complex and large cloud environments. Additionally, we use CYE to reproduce real-world situations into the scenario and apply reinforcement learning for training automated intelligence defense agents. Full article
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Review

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24 pages, 424 KiB  
Review
Understanding the Role of Demographic and Psychological Factors in Users’ Susceptibility to Phishing Emails: A Review
by Alexandros Kavvadias and Theodore Kotsilieris
Appl. Sci. 2025, 15(4), 2236; https://doi.org/10.3390/app15042236 - 19 Feb 2025
Viewed by 244
Abstract
Phishing emails are malicious email messages that aim to deceive users into revealing sensitive information by imitating legitimate emails. These emails are usually among the first steps in most cyberattacks, often appearing as an urgent message, seemingly from reputable sources, in order to [...] Read more.
Phishing emails are malicious email messages that aim to deceive users into revealing sensitive information by imitating legitimate emails. These emails are usually among the first steps in most cyberattacks, often appearing as an urgent message, seemingly from reputable sources, in order to provoke an immediate action from the recipient. Their manipulative nature leverages social engineering techniques to exploit human psychological weaknesses, personality traits, and a range of cognitive, behavioral, and technical vulnerabilities. In this review, the factors that contribute to users’ susceptibility to phishing attacks were investigated. The study focuses on exploring how demographic and psychological factors influence individuals’ vulnerability to phishing emails, with the goal of identifying and categorizing the key factors that increase susceptibility. Twenty-seven studies were examined, revealing that demographic factors, behavioral tendencies, psychological traits and contextual elements play a key role on the users’ susceptibility in phishing emails. The results vary according to the type of methodology that has been used, indicating a need for further investigation and refinement in each respective procedure. Significant investigation has been conducted in identifying the factors contributing to users’ susceptibility to phishing emails, and existing studies do not fully cover the complexity of the topic. There is more to be studied regarding these factors, especially in understanding their complex interactions and impacts across different contexts. Further research is essential so that we may be able to more accurately predict users’ characteristics and the factors that make someone more susceptible to phishing and thus more vulnerable to phishing email attacks. Full article
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