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Cybersecurity and Secure Information Systems: Challenges and Solutions in Digital...
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Cybersecurity and Secure Information Systems: Challenges and Solutions in Digital Environment

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Engineering".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 13387

Special Issue Editor

Dr. Anas M Alsobeh

E-Mail Website
Guest Editor
Information Technology, School of Computing, Southern Illinois University Carbondale, 1365 Douglas Drive, Carbondale, IL 62901, USA
Interests: software design and modeling; data analysis; web technology; security analysis; AI; machine learning; cloud computing

Special Issue Information

Dear Colleagues,

In today's interconnected world, cybersecurity has become a paramount concern for businesses, governments, and individuals alike. As technology continues to advance at an unprecedented pace, the sophistication and frequency of cyber threats do too. This Special Issue of Systems invites contributions centered around the field of critical cybersecurity and secure information systems, addressing the persistent and evolving challenges in the digital environment. The research findings and solutions we encourage you to present should not only analyze the current state of cybersecurity but also propose innovative approaches that can effectively mitigate the risks and vulnerabilities faced by organizations and individuals. Your contributions should be focused, yet they should also effectively highlight the complex patterns and interdependencies within the cybersecurity landscape, like in impressionist art. The solutions and strategies proposed should be practical; thus, they should not just be theoretical concepts but should be implementable through the available technological and policy levers in the system.

In this Special Issue, original research articles, review papers, case studies, and conceptual papers are welcome. Research areas may include (but are not limited to) the following:

  • Cryptography, encryption, and key management techniques;
  • Network security, intrusion detection, and threat intelligence;
  • Cloud security, privacy, and trust management;
  • Blockchain and distributed ledger technologies for secure systems;
  • Secure software development, testing, and vulnerability assessment;
  • Human factors, social engineering, and cybersecurity awareness;
  • Cybercrime, digital forensics, and incident response;
  • Privacy, data protection, and compliance (e.g., GDPR and CCPA);
  • Cybersecurity policies, governance, and risk management frameworks;
  • Emerging threats, vulnerabilities, and countermeasures;
  • AI, machine learning, and big data analytics for cybersecurity;
  • IoT, embedded systems, and hardware security;
  • Quantum computing and post-quantum cryptography;
  • Cybersecurity education, training, and workforce development;
  • Applications of large language models (LLMs) in cybersecurity;
  • Natural language processing (NLP) techniques for cyber threat intelligence;
  • Secure and privacy-preserving LLM architectures and training methods;
  • Ethical considerations and the responsible deployment of LLMs in cybersecurity;
  • Diffusion models for adversarial machine learning in cybersecurity;
  • Digital forensics;
  • Generative adversarial networks (GANs) for cybersecurity applications;
  • Adversarial attacks and defenses in machine-learning-based cybersecurity systems.

Dr. Anas M Alsobeh
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. Systems 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 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
  • digital environment
  • cyber threats
  • cloud security
  • network security
  • privacy protection
  • cryptography
  • AI in cybersecurity
  • blockchain security
  • LLMs in cybersecurity
  • cybercrime detection

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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

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Research

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20 pages, 2912 KiB  
Article
Effective Context-Aware File Path Embeddings for Anomaly Detection
by Ra-Kyung Lee, Hyun-Min Song and Taek-Young Youn
Systems 2025, 13(6), 403; https://doi.org/10.3390/systems13060403 - 23 May 2025
Viewed by 251
Abstract
In digital forensics, especially Windows forensics, identifying anomalous file paths is crucial when dealing with large-scale data. Traditional static embedding methods, which aggregate token-level representations, discard hierarchical and sequential relationships in file paths, leading to misclassification of anomalies. This study introduces a Transformer-based [...] Read more.
In digital forensics, especially Windows forensics, identifying anomalous file paths is crucial when dealing with large-scale data. Traditional static embedding methods, which aggregate token-level representations, discard hierarchical and sequential relationships in file paths, leading to misclassification of anomalies. This study introduces a Transformer-based sequence modeling approach to classify anomalous file paths, addressing these limitations by preserving positional and contextual relationships. File paths from the NTFS Master File Table (MFT) were embedded using FastText to capture structural and contextual dependencies. Unlike static embeddings, the proposed method processes file paths as structured sequences to enhance anomaly detection accuracy. Extensive experiments showed that Transformer models generally outperformed traditional methods in detecting structured anomalies. The Transformer model with FastText embeddings (32 dimensions) achieved an accuracy of 0.9781 and an F1-score of 0.9782, while Random Forest with FastText embeddings (64 dimensions) achieved an accuracy of 0.9729 and an F1-score of 0.9729. These findings suggest that a hybrid anomaly detection framework combining Transformer-based models with traditional techniques could enhance robustness in forensic investigations. Future research should explore combining both methods to improve adaptability across diverse forensic scenarios. Full article
(This article belongs to the Special Issue Cybersecurity and Secure Information Systems: Challenges and Solutions in Digital Environment)
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27 pages, 1843 KiB  
Article
Multi-Layered Security Framework Combining Steganography and DNA Coding
by Bhavya Kallapu, Avinash Nanda Janardhan, Rama Moorthy Hejamadi, Krishnaraj Rao Nandikoor Shrinivas, Saritha, Raghunandan Kemmannu Ramesh and Lubna A. Gabralla
Systems 2025, 13(5), 341; https://doi.org/10.3390/systems13050341 - 1 May 2025
Viewed by 399
Abstract
With the rapid expansion of digital communication and data sharing, ensuring robust security for sensitive information has become increasingly critical, particularly when data are transmitted over public networks. Traditional encryption techniques are increasingly vulnerable to evolving cyber threats, making single-layer security mechanisms less [...] Read more.
With the rapid expansion of digital communication and data sharing, ensuring robust security for sensitive information has become increasingly critical, particularly when data are transmitted over public networks. Traditional encryption techniques are increasingly vulnerable to evolving cyber threats, making single-layer security mechanisms less effective. This study proposes a multi-layered security approach that integrates cryptographic and steganographic techniques to enhance data protection. The framework leverages advanced methods such as encrypted data embedding in images, DNA sequence coding, QR codes, and least significant bit (LSB) steganography. To evaluate its effectiveness, experiments were conducted using text messages, text files, and images, with security assessments based on PSNR, MSE, SNR, and encryption–decryption times for text data. Image security was analyzed through visual inspection, correlation, entropy, standard deviation, key space analysis, randomness, and differential analysis. The proposed method demonstrated strong resilience against differential cryptanalysis, achieving high NPCR values (99.5784%, 99.4292%, and 99.5784%) and UACI values (33.5873%, 33.5149%, and 33.3745%), indicating robust diffusion and confusion properties. These results highlight the reliability and effectiveness of the proposed framework in safeguarding data integrity and confidentiality, providing a promising direction for future cryptographic research. Full article
(This article belongs to the Special Issue Cybersecurity and Secure Information Systems: Challenges and Solutions in Digital Environment)
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30 pages, 526 KiB  
Article
Optimizing Security of Radio Frequency Identification Systems in Assistive Devices: A Novel Unidirectional Systolic Design for Dickson-Based Field Multiplier
by Atef Ibrahim and Fayez Gebali
Systems 2025, 13(3), 154; https://doi.org/10.3390/systems13030154 - 25 Feb 2025
Cited by 1 | Viewed by 556
Abstract
The emergence of the Internet of Things (IoT) technologies has greatly enhanced the lives of individuals with disabilities by leveraging radio frequency identification (RFID) systems to improve autonomy and access to essential services. However, these advancements also pose significant security risks, particularly through [...] Read more.
The emergence of the Internet of Things (IoT) technologies has greatly enhanced the lives of individuals with disabilities by leveraging radio frequency identification (RFID) systems to improve autonomy and access to essential services. However, these advancements also pose significant security risks, particularly through side-channel attacks that exploit weaknesses in the design and operation of RFID tags and readers, potentially jeopardizing sensitive information. To combat these threats, several solutions have been proposed, including advanced cryptographic protocols built on cryptographic algorithms such as elliptic curve cryptography. While these protocols offer strong protection and help minimize data leakage, they often require substantial computational resources, making them impractical for low-cost RFID tags. Therefore, it is essential to focus on the efficient implementation of cryptographic algorithms, which are fundamental to most encryption systems. Cryptographic algorithms primarily depend on various finite field operations, including field multiplication, field inversion, and field division. Among these operations, field multiplication is especially crucial, as it forms the foundation for executing other field operations, making it vital for the overall performance and security of the cryptographic framework. The method of implementing field multiplication operation significantly influences the system’s resilience against side-channel attacks; for instance, implementation using unidirectional systolic array structures can provide enhanced error detection capabilities, improving resistance to side-channel attacks compared to traditional bidirectional multipliers. Therefore, this research aims to develop a novel unidirectional systolic array structure for the Dickson basis multiplier, which is anticipated to achieve lower space and power consumption, facilitating the efficient and secure implementation of computationally intensive cryptographic algorithms in RFID systems with limited resources. This advancement is crucial as RFID technology becomes increasingly integrated into various IoT applications for individuals with disabilities, including secure identification and access control. Full article
(This article belongs to the Special Issue Cybersecurity and Secure Information Systems: Challenges and Solutions in Digital Environment)
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23 pages, 3250 KiB  
Article
A Generalized Framework for Adversarial Attack Detection and Prevention Using Grad-CAM and Clustering Techniques
by Jeong-Hyun Sim and Hyun-Min Song
Systems 2025, 13(2), 88; https://doi.org/10.3390/systems13020088 - 31 Jan 2025
Cited by 1 | Viewed by 1217
Abstract
Through advances in AI-based computer vision technology, the performance of modern image classification models has surpassed human perception, making them valuable in various fields. However, adversarial attacks, which involve small changes to images that are hard for humans to perceive, can cause classification [...] Read more.
Through advances in AI-based computer vision technology, the performance of modern image classification models has surpassed human perception, making them valuable in various fields. However, adversarial attacks, which involve small changes to images that are hard for humans to perceive, can cause classification models to misclassify images. Considering the availability of classification models that use neural networks, it is crucial to prevent adversarial attacks. Recent detection methods are only effective for specific attacks or cannot be applied to various models. Therefore, in this paper, we proposed an attention mechanism-based method for detecting adversarial attacks. We utilized a framework using an ensemble model, Grad-CAM and calculated the silhouette coefficient for detection. We applied this method to Resnet18, Mobilenetv2, and VGG16 classification models that were fine-tuned on the CIFAR-10 dataset. The average performance demonstrated that Mobilenetv2 achieved an F1-Score of 0.9022 and an accuracy of 0.9103, Resnet18 achieved an F1-Score of 0.9124 and an accuracy of 0.9302, and VGG16 achieved an F1-Score of 0.9185 and an accuracy of 0.9252. The results demonstrated that our method not only detects but also prevents adversarial attacks by mitigating their effects and effectively restoring labels. Full article
(This article belongs to the Special Issue Cybersecurity and Secure Information Systems: Challenges and Solutions in Digital Environment)
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21 pages, 780 KiB  
Article
Enhancing Cybersecurity: Hybrid Deep Learning Approaches to Smishing Attack Detection
by Tanjim Mahmud, Md. Alif Hossen Prince, Md. Hasan Ali, Mohammad Shahadat Hossain and Karl Andersson
Systems 2024, 12(11), 490; https://doi.org/10.3390/systems12110490 - 14 Nov 2024
Cited by 11 | Viewed by 3045
Abstract
Smishing attacks, a sophisticated form of cybersecurity threats conducted via Short Message Service (SMS), have escalated in complexity with the widespread adoption of mobile devices, making it increasingly challenging for individuals to distinguish between legitimate and malicious messages. Traditional phishing detection methods, such [...] Read more.
Smishing attacks, a sophisticated form of cybersecurity threats conducted via Short Message Service (SMS), have escalated in complexity with the widespread adoption of mobile devices, making it increasingly challenging for individuals to distinguish between legitimate and malicious messages. Traditional phishing detection methods, such as feature-based, rule-based, heuristic, and blacklist approaches, have struggled to keep pace with the rapidly evolving tactics employed by attackers. To enhance cybersecurity and address these challenges, this paper proposes a hybrid deep learning approach that combines Bidirectional Gated Recurrent Units (Bi-GRUs) and Convolutional Neural Networks (CNNs), referred to as CNN-Bi-GRU, for the accurate identification and classification of smishing attacks. The SMS Phishing Collection dataset was used, with a preparatory procedure involving the transformation of unstructured text data into numerical representations and the training of Word2Vec on preprocessed text. Experimental results demonstrate that the proposed CNN-Bi-GRU model outperforms existing approaches, achieving an overall highest accuracy of 99.82% in detecting SMS phishing messages. This study provides an empirical analysis of the effectiveness of hybrid deep learning techniques for SMS phishing detection, offering a more precise and efficient solution to enhance cybersecurity in mobile communications. Full article
(This article belongs to the Special Issue Cybersecurity and Secure Information Systems: Challenges and Solutions in Digital Environment)
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42 pages, 2540 KiB  
Systematic Review
Recent Trends in Information and Cyber Security Maturity Assessment: A Systematic Literature Review
by Alenka Brezavšček and Alenka Baggia
Systems 2025, 13(1), 52; https://doi.org/10.3390/systems13010052 - 15 Jan 2025
Cited by 2 | Viewed by 4155
Abstract
This work represents a comprehensive and systematic literature review (SLR) that follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for research assessing information and cyber security maturity. The period from 2012 to 2024 was considered and the final collection [...] Read more.
This work represents a comprehensive and systematic literature review (SLR) that follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for research assessing information and cyber security maturity. The period from 2012 to 2024 was considered and the final collection of 96 studies was taken into account. Our findings were summarised in two stages, a quantitative analysis and a qualitative synthesis. In the first part, various quantitative indicators were used to analyse the evolution of the information and cyber security maturity assessment domain over the last twelve years. The qualitative synthesis, which was limited to 36 research papers, categorises the studies into three key areas: the development of new maturity models, the implementation of established models and frameworks, and the advancement of methodologies to support maturity assessments. The findings reveal significant progress in sector-specific customisation, the growing importance of lightweight models for small and medium-sized enterprises (SMEs), and the integration of emerging technologies. This study provides important insights into the evolving landscape of information and cyber security maturity assessment and provides actionable recommendations for academia and industry to improve security resilience and support the adoption of tailored, effective maturity models. Full article
(This article belongs to the Special Issue Cybersecurity and Secure Information Systems: Challenges and Solutions in Digital Environment)
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24 pages, 2160 KiB  
Systematic Review
Sustainability and Information Systems in the Context of Smart Business: A Systematic Review
by Aws A. Magableh, Afnan Y. Audeh, Lana L. Ghraibeh, Mohammed Akour and Ahmed Shihab Albahri
Systems 2024, 12(10), 427; https://doi.org/10.3390/systems12100427 - 12 Oct 2024
Cited by 1 | Viewed by 2198
Abstract
In recent years, calls have increased for adherence to standards that ensure sustainability, including the global initiative presented by the United Nations with 17 Sustainable Development Goals (SDGs) to ensure a more sustainable future. Achieving these goals is extremely important, as institutions have [...] Read more.
In recent years, calls have increased for adherence to standards that ensure sustainability, including the global initiative presented by the United Nations with 17 Sustainable Development Goals (SDGs) to ensure a more sustainable future. Achieving these goals is extremely important, as institutions have sought to integrate technology, especially business intelligence, into their operations to ensure their achievement. This study aims to provide a systematic literature review of the intersection of information systems and sustainability in business intelligence. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was utilized to select high-quality studies from various databases, including ScienceDirect, IEEE Xplore, and Scopus, to be included in this review. The methodology resulted in 32 studies taxonomized into four main categories covering different aspects of the intersection of information systems and sustainability. This study discusses integrating information systems and sustainability in various sectors, such as tourism, health, urban, and other sectors, with different technologies, such as Blockchain, IoT, Industry 4.0, and other innovations. Moreover, the information system types implemented to support sustainability practices in different domains are highlighted. Full article
(This article belongs to the Special Issue Cybersecurity and Secure Information Systems: Challenges and Solutions in Digital Environment)
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