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A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Security and Privacy".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 30811

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Special Issue Editors

Dr. Mohamed Hammad

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Faculty of Computers and Information, Menoufia University‬, Shebin El-Koom 32511, Egypt
Interests: biometrics; pattern recognition; deep learning; machine learning; AI
Special Issues, Collections and Topics in MDPI journals

Dr. Ahmed A. Abd El-Latif

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Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom 32511, Egypt
Interests: quantum information processing; information security; cybersecurity; information hiding; biometrics; internet of things (IoT); big data; blockchain; forensic analysis in digital images
Special Issues, Collections and Topics in MDPI journals

Dr. Abdelhamied Ashraf Ateya

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Guest Editor

Department of Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt
Interests: 5G wireless communications; tactile internet; vehicular networks; SDN; MEC
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mohammed ElAffendi

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Guest Editor

EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
Interests: cybersecurity; artificial intelligence; deep learning; complexity science; data science; natural language processing

Special Issue Information

Dear Colleagues,

This Special Issue will present extended versions of selected papers presented at the Second International Conference on Cybersecurity, Cybercrimes, and Smart Emerging Technologies (CCSET2023). The objectives of CCSET 2023 are to provide a premier international platform for deliberations on strategies, recent trends, innovative approaches, discussions, and presentations on the most recent cybersecurity, cybercrime, and emerging technologies challenges and developments from the perspective of providing real-world security awareness. Moreover, the motivation to organize this conference is to promote research by sharing innovative ideas across all levels of the scientific community and to provide opportunities to develop creative solutions for various cybersecurity and smart emerging technologies problems. Authors of invited papers should be aware that the final submitted manuscript must provide a minimum of 50% new content and not exceed 30% copy/paste from the proceedings paper. In addition, authors outside the conference are also welcome to submit their papers within the scope of the issue.

Topics:

Cybersecurity and cybercrime
Smart emerging technologies
Social cybersecurity with NLP and deep machine learning
Blockchain for cybersecurity
Web application security
5G/6G edge/fog computing-enabled Internet of Things
Cloud computing and applications
Artificial intelligence for smart emerging technologies
Security in medical imaging
Biometric security
Fake profiles, Honey accounts and unlawful accounts on social media
Advances in deep machine learning for cybersecurity

Dr. Mohamed Hammad
Dr. Ahmed A. Abd El-Latif
Dr. Abdelhamied Ashraf Ateya
Prof. Dr. Mohammed ElAffendi
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. Information is an international peer-reviewed open access monthly 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 1600 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.

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Published Papers (9 papers)

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Research

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27 pages, 743 KiB

Open AccessArticle

Blockchain-Based Privacy-Preserving Authentication and Access Control Model for E-Health Users

by Abdullah Alabdulatif

Information 2025, 16(3), 219; https://doi.org/10.3390/info16030219 - 13 Mar 2025

Cited by 1 | Viewed by 736

Abstract

The advancement of e-health systems has resulted in substantial enhancements in healthcare delivery via effective data management and accessibility. The use of digital health solutions presents dangers to sensitive health information, including unauthorised access, privacy violations, and security weaknesses. This research presents a blockchain-based paradigm for privacy-preserving authentication and access control specifically designed for e-health systems. The architecture utilises the Ethereum blockchain, smart contracts, blind signatures, Proof of Authority (PoA) consensus, and one-way hash functions to improve data integrity, security, and privacy in a decentralised framework. The proposed methodology addresses computational efficiency and scalability issues via the implementation of lightweight cryptographic techniques, achieving an average authentication delay of 0.059 milliseconds, which represents a 4000-fold improvement compared to current approaches. The model exhibits a significant decrease in memory use, requiring just 0.0198 MB in contrast to the 96.98 MB required by benchmark models, and attains an average signature verification duration of 0.00092 milliseconds. The findings demonstrate the model’s capability for safe, efficient, and scalable applications in e-health, which guarantees privacy and adherence to regulatory norms. Full article

(This article belongs to the Special Issue Cybersecurity, Cybercrimes, and Smart Emerging Technologies)

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19 pages, 1061 KiB

Open AccessArticle

Decentralized Trace-Resistant Self-Sovereign Service Provisioning for Next-Generation Federated Wireless Networks

by Efat Fathalla and Mohamed Azab

Information 2025, 16(3), 159; https://doi.org/10.3390/info16030159 - 20 Feb 2025

Viewed by 473

Abstract

With the advent of NextG wireless networks, the reliance on centralized identity and service management systems poses significant challenges, including limited interoperability, increased privacy vulnerabilities, and the risk of unauthorized tracking or monitoring of user activity. To address these issues, there is a critical need for a decentralized framework that empowers users with self-sovereignty over their subscription information while maintaining trust and privacy among network entities. This article presents a novel framework to enable Self-Sovereign Federated NextG (SSFXG) wireless communication networks. The SSFXG framework separates identity management from the service management layer typically controlled by network operators to foster interoperability functionalities with enhanced privacy and trace-resistant assurances in the NextG landscape. The proposed model relies on blockchain technology as an infrastructure to enable single-authority-free service provisioning and boost mutual trust among federated network components. Further, the SSFXG framework facilitates subscribers’ self-sovereignty over their subscription information while ensuring anonymity and enhanced privacy preservation, avoiding unnecessary network activity monitoring or tracking. Preliminary evaluations demonstrated the effectiveness and efficiency of the proposed framework, making it a promising solution for advancing secure and interoperable NextG wireless networks. Full article

(This article belongs to the Special Issue Cybersecurity, Cybercrimes, and Smart Emerging Technologies)

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26 pages, 1504 KiB

Open AccessArticle

Encrypted Engagement: Mapping Messaging App Use in European News Consumption Patterns

by Răzvan Rughiniș, Dinu Țurcanu, Simona-Nicoleta Vulpe and Alexandru Radovici

Information 2025, 16(1), 48; https://doi.org/10.3390/info16010048 - 14 Jan 2025

Viewed by 916

Abstract

This study examines the emerging role of messaging apps and end-to-end encryption in news consumption patterns across the European Union. Using data from the Flash Eurobarometer 3153 “Media and News Survey 2023”, we employed K-Means cluster analysis to identify five distinct news consumer profiles. Our findings reveal that while messaging apps are used by 15% of EU residents for news consumption, their adoption varies significantly across demographic groups and regions. Notably, omnivorous news consumers show the highest usage (61%) and trust in these platforms, indicating a complementary role to traditional news sources. The study highlights a generational divide, with younger users and those still in education showing a stronger preference for messaging apps. Surprisingly, individuals without formal education also demonstrate high usage, challenging assumptions about the digital divide. This research offers updated, large-scale information on the evolving European news ecosystem, where private, encrypted channels are gaining importance alongside public platforms. Our findings have significant implications for media strategies, policymaking, and understanding the future of news dissemination in an increasingly digital and privacy-conscious Europe. Full article

(This article belongs to the Special Issue Cybersecurity, Cybercrimes, and Smart Emerging Technologies)

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21 pages, 10483 KiB

Open AccessArticle

Evading Cyber-Attacks on Hadoop Ecosystem: A Novel Machine Learning-Based Security-Centric Approach towards Big Data Cloud

by Neeraj A. Sharma, Kunal Kumar, Tanzim Khorshed, A B M Shawkat Ali, Haris M. Khalid, S. M. Muyeen and Linju Jose

Information 2024, 15(9), 558; https://doi.org/10.3390/info15090558 - 10 Sep 2024

Cited by 1 | Viewed by 1246

Abstract

The growing industry and its complex and large information sets require Big Data (BD) technology and its open-source frameworks (Apache Hadoop) to (1) collect, (2) analyze, and (3) process the information. This information usually ranges in size from gigabytes to petabytes of data. However, processing this data involves web consoles and communication channels which are prone to intrusion from hackers. To resolve this issue, a novel machine learning (ML)-based security-centric approach has been proposed to evade cyber-attacks on the Hadoop ecosystem while considering the complexity of Big Data in Cloud (BDC). An Apache Hadoop-based management interface “Ambari” was implemented to address the variation and distinguish between attacks and activities. The analyzed experimental results show that the proposed scheme effectively (1) blocked the interface communication and retrieved the performance measured data from (2) the Ambari-based virtual machine (VM) and (3) BDC hypervisor. Moreover, the proposed architecture was able to provide a reduction in false alarms as well as cyber-attack detection. Full article

(This article belongs to the Special Issue Cybersecurity, Cybercrimes, and Smart Emerging Technologies)

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19 pages, 2187 KiB

Open AccessArticle

Towards an Innovative Model for Cybersecurity Awareness Training

by Hamed Taherdoost

Information 2024, 15(9), 512; https://doi.org/10.3390/info15090512 - 23 Aug 2024

Cited by 2 | Viewed by 5130

Abstract

The rapid evolution of cybersecurity threats poses a significant challenge to organizations and individuals, necessitating strengthening defense mechanisms against malicious operations. Amidst this ever-changing environment, the importance of implementing efficacious cybersecurity awareness training has escalated dramatically. This paper presents the Integrated Cybersecurity Awareness Training (iCAT) model, which leverages knowledge graphs, serious games, and gamification to enhance cybersecurity training. The iCAT model’s micro-learning module increases flexibility and accessibility, while real-time progress monitoring and adaptive feedback ensure effective learning outcomes. Evaluations show improved participant engagement and knowledge retention, making iCAT a practical and efficient solution for cybersecurity challenges. With an emphasis on adaptability and applicability, iCAT provides organizations in search of accessible and efficient cybersecurity awareness training with a streamlined approach. Full article

(This article belongs to the Special Issue Cybersecurity, Cybercrimes, and Smart Emerging Technologies)

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27 pages, 19187 KiB

Open AccessArticle

Analyzing Tor Browser Artifacts for Enhanced Web Forensics, Anonymity, Cybersecurity, and Privacy in Windows-Based Systems

by Muhammad Shanawar Javed, Syed Muhammad Sajjad, Danish Mehmood, Khawaja Mansoor, Zafar Iqbal, Muhammad Kazim and Zia Muhammad

Information 2024, 15(8), 495; https://doi.org/10.3390/info15080495 - 19 Aug 2024

Cited by 2 | Viewed by 4299

Abstract

The Tor browser is widely used for anonymity, providing layered encryption for enhanced privacy. Besides its positive uses, it is also popular among cybercriminals for illegal activities such as trafficking, smuggling, betting, and illicit trade. There is a need for Tor Browser forensics to identify its use in unlawful activities and explore its consequences. This research analyzes artifacts generated by Tor on Windows-based systems. The methodology integrates forensic techniques into incident responses per NIST SP (800-86), exploring areas such as registry, storage, network, and memory using tools like bulk-extractor, autopsy, and regshot. We propose an automated PowerShell script that detects Tor usage and retrieves artifacts with minimal user interaction. Finally, this research performs timeline analysis and artifact correlation for a contextual understanding of event sequences in memory and network domains, ultimately contributing to improved incident response and accountability. Full article

(This article belongs to the Special Issue Cybersecurity, Cybercrimes, and Smart Emerging Technologies)

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24 pages, 4230 KiB

Open AccessArticle

Understanding Local Government Cybersecurity Policy: A Concept Map and Framework

by Sk Tahsin Hossain, Tan Yigitcanlar, Kien Nguyen and Yue Xu

Information 2024, 15(6), 342; https://doi.org/10.3390/info15060342 - 10 Jun 2024

Cited by 5 | Viewed by 3888

Abstract

Cybersecurity is a crucial concern for local governments as they serve as the primary interface between public and government services, managing sensitive data and critical infrastructure. While technical safeguards are integral to cybersecurity, the role of a well-structured policy is equally important as it provides structured guidance to translate technical requirements into actionable protocols. This study reviews local governments’ cybersecurity policies to provide a comprehensive assessment of how these policies align with the National Institute of Standards and Technology’s Cybersecurity Framework 2.0, which is a widely adopted and commonly used cybersecurity assessment framework. This review offers local governments a mirror to reflect on their cybersecurity stance, identifying potential vulnerabilities and areas needing urgent attention. This study further extends the development of a cybersecurity policy framework, which local governments can use as a strategic tool. It provides valuable information on crucial cybersecurity elements that local governments must incorporate into their policies to protect confidential data and critical infrastructure. Full article

(This article belongs to the Special Issue Cybersecurity, Cybercrimes, and Smart Emerging Technologies)

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18 pages, 1456 KiB

Open AccessArticle

Insights into Cybercrime Detection and Response: A Review of Time Factor

by Hamed Taherdoost

Information 2024, 15(5), 273; https://doi.org/10.3390/info15050273 - 12 May 2024

Cited by 5 | Viewed by 6075

Abstract

Amidst an unprecedented period of technological progress, incorporating digital platforms into diverse domains of existence has become indispensable, fundamentally altering the operational processes of governments, businesses, and individuals. Nevertheless, the swift process of digitization has concurrently led to the emergence of cybercrime, which takes advantage of weaknesses in interconnected systems. The growing dependence of society on digital communication, commerce, and information sharing has led to the exploitation of these platforms by malicious actors for hacking, identity theft, ransomware, and phishing attacks. With the growing dependence of organizations, businesses, and individuals on digital platforms for information exchange, commerce, and communication, malicious actors have identified the susceptibilities present in these systems and have begun to exploit them. This study examines 28 research papers focusing on intrusion detection systems (IDS), and phishing detection in particular, and how quickly responses and detections in cybersecurity may be made. We investigate various approaches and quantitative measurements to comprehend the link between reaction time and detection time and emphasize the necessity of minimizing both for improved cybersecurity. The research focuses on reducing detection and reaction times, especially for phishing attempts, to improve cybersecurity. In smart grids and automobile control networks, faster attack detection is important, and machine learning can help. It also stresses the necessity to improve protocols to address increasing cyber risks while maintaining scalability, interoperability, and resilience. Although machine-learning-based techniques have the potential for detection precision and reaction speed, obstacles still need to be addressed to attain real-time capabilities and adjust to constantly changing threats. To create effective defensive mechanisms against cyberattacks, future research topics include investigating innovative methodologies, integrating real-time threat intelligence, and encouraging collaboration. Full article

(This article belongs to the Special Issue Cybersecurity, Cybercrimes, and Smart Emerging Technologies)

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Review

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48 pages, 894 KiB

Open AccessReview

Earlier Decision on Detection of Ransomware Identification: A Comprehensive Systematic Literature Review

by Latifa Albshaier, Seetah Almarri and M. M. Hafizur Rahman

Information 2024, 15(8), 484; https://doi.org/10.3390/info15080484 - 14 Aug 2024

Cited by 5 | Viewed by 5673

Abstract

Cybersecurity is normally defined as protecting systems against all kinds of cyberattacks; however, due to the rapid and permanent expansion of technology and digital transformation, the threats are also increasing. One of those new threats is ransomware, which is a form of malware that aims to steal user’s money. Ransomware is a form of malware that encrypts a victim’s files. The attacker then demands a ransom from the victim to restore access to the data upon a large payment. Ransomware is a way of stealing money in which a user’s files are encrypted and the decrypted key is held by the attacker until a ransom amount is paid by the victim. This systematic literature review (SLR) highlights recent papers published between 2020 and 2024. This paper examines existing research on early ransomware detection methods, focusing on the signs, frameworks, and techniques used to identify and detect ransomware before it causes harm. By analyzing a wide range of academic papers, industry reports, and case studies, this review categorizes and assesses the effectiveness of different detection methods, including those based on signatures, behavior patterns, and machine learning (ML). It also looks at new trends and innovative strategies in ransomware detection, offering a classification of detection techniques and pointing out the gaps in current research. The findings provide useful insights for cybersecurity professionals and researchers, helping guide future efforts to develop strong and proactive ransomware detection systems. This review emphasizes the need for ongoing improvements in detection technologies to keep up with the constantly changing ransomware threat landscape. Full article

(This article belongs to the Special Issue Cybersecurity, Cybercrimes, and Smart Emerging Technologies)

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