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Mathematics
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Research and Advances in Network Security
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Research and Advances in Network Security

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1141

Special Issue Editor

Dr. Mohamed Rahouti

E-Mail Website
Guest Editor
Department of Computer & Information Science, Fordham University, Bronx, NY 10458, USA
Interests: computer networking and security; software-defined networking; blockchain technology; artificial intelligence; machine learning; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the rapidly evolving landscape of digital technology, network security remains a critical concern for individuals, enterprises, and governments. As we navigate through an era where cyber threats are becoming more sophisticated and pervasive, it is imperative to advance our understanding and capabilities in protecting networks. This Special Issue on "Research and Advances in Network Security" aims to showcase cutting-edge research that addresses the multifaceted challenges in this field. The contributions will encompass a broad spectrum of topics, ranging from innovative security protocols to advances in cryptographic techniques, and from intrusion detection systems to the application of artificial intelligence in cybersecurity.

The focus areas of this Special Issue include the following (but are not limited to them):

  • Innovative security protocols for diverse network environments.
  • Enhancing data integrity through novel security protocols.
  • Confidentiality enhancements in contemporary networking scenarios.
  • Advancements in cryptography for secure networks and robust data protection.
  • Quantum-resistant algorithms for future-proof encryption of network data.
  • Advances in signature-based intrusion detection systems (IDPSs).
  • Anomaly-based IDPS advancements for proactive threat detection.
  • Hybrid detection systems for comprehensive network security.
  • AI-driven approaches for predicting and mitigating cyberattacks.
  • Automated threat detection using artificial intelligence in cybersecurity.
  • Case studies illustrating successful network security strategies and implementations.
  • Generative AI for identifying trends and evaluating the current state of network security.
  • Bridging theory and practice in network security through practical applications.
  • Informing research agendas and policy decisions in network security.

Contributions to this Special Issue will provide a comprehensive panorama of the latest research and technological advancements in network security. By bringing together experts from academia, industry, and the government, this Special Issue aims to foster a collaborative environment that will fuel innovation and develop solutions that are practical, efficient, and scalable. The collective insight from these papers will not only advance scientific knowledge but also contribute to the formulation of more resilient security strategies, safeguarding our digital future against an ever-evolving threat landscape.

We invite researchers and practitioners from around the world to contribute their work to this Special Issue. By sharing your research, you are contributing to a vital dialogue on network security that promises to influence best practices and policymaking, shaping a safer digital world for tomorrow.

Dr. Mohamed Rahouti
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. Mathematics 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 2600 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

  • innovative security protocols
  • contemporary networking scenarios
  • secure networks
  • robust data protection
  • quantum-resistant algorithms
  • IDPSs
  • AI-driven approaches

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

Published Papers (2 papers)

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22 pages, 1109 KiB  
Article
Optimizing Intrusion Detection in IoMT Networks Through Interpretable and Cost-Aware Machine Learning
by Abdelatif Hafid, Mohamed Rahouti and Mohammed Aledhari
Mathematics 2025, 13(10), 1574; https://doi.org/10.3390/math13101574 - 10 May 2025
Viewed by 300
Abstract
The rise of the Internet of Medical Things (IoMT) has enhanced healthcare delivery but also exposed critical cybersecurity vulnerabilities. Detecting attacks in such environments demands accurate, interpretable, and cost-efficient models. This paper addresses the critical challenges in network security, particularly in IoMT, through [...] Read more.
The rise of the Internet of Medical Things (IoMT) has enhanced healthcare delivery but also exposed critical cybersecurity vulnerabilities. Detecting attacks in such environments demands accurate, interpretable, and cost-efficient models. This paper addresses the critical challenges in network security, particularly in IoMT, through advanced machine learning (ML) approaches. We propose a high-performance cybersecurity framework leveraging a carefully fine-tuned XGBoost classifier to detect malicious attacks with superior predictive accuracy while maintaining interpretability. Our comprehensive evaluation compares the proposed model with a well-regularized Logistic Regression baseline using key performance metrics. Additionally, we analyze the security-cost trade-off in designing ML systems for threat detection and employ SHAP (SHapley Additive exPlanations) to identify key features driving predictions. We further introduce a late fusion approach based on max voting that effectively combines the strengths of both models. Results demonstrate that while XGBoost achieves higher accuracy (0.97) and recall (1.00) compared to Logistic Regression, our late fusion model provides a more balanced performance with improved precision (0.98) and reduced false negatives, making it particularly suitable for security-sensitive applications. This work contributes to developing robust, interpretable, and efficient ML solutions for addressing evolving cybersecurity challenges in networked environments. Full article
(This article belongs to the Special Issue Research and Advances in Network Security)
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27 pages, 673 KiB  
Article
A Wide and Weighted Deep Ensemble Model for Behavioral Drifting Ransomware Attacks
by Umara Urooj, Bander Ali Saleh Al-rimy, Mazen Gazzan, Anazida Zainal, Eslam Amer, Mohammed Almutairi, Stavros Shiaeles and Frederick Sheldon
Mathematics 2025, 13(7), 1037; https://doi.org/10.3390/math13071037 - 22 Mar 2025
Viewed by 452
Abstract
Ransomware is a type of malware that leverages encryption to execute its attacks. Its continuous evolution underscores its dynamic and ever-changing nature. The evolving variants use varying timelines to launch attacks and associate them with varying attack patterns. Detecting early evolving variants also [...] Read more.
Ransomware is a type of malware that leverages encryption to execute its attacks. Its continuous evolution underscores its dynamic and ever-changing nature. The evolving variants use varying timelines to launch attacks and associate them with varying attack patterns. Detecting early evolving variants also leads to incomplete attack patterns. To develop an early detection model for behavioral drifting ransomware attacks, a detection model should be able to detect evolving ransomware variants. To consider the behavioral drifting problem of ransomware attacks, a model should be able to generalize the behavior of significant features comprehensively. Existing solutions were developed by using either a whole attack pattern or a fraction of an attack pattern. Likewise, they were also designed using historical data, which can make these solutions outdated or suffer from low accuracy for behavioral drift ransomware attacks. The detection models created using a fraction of the pre-encryption data also can not generalize the attack behavior of evolving ransomware variants. There is a need to develop an early detection model that can detect evolving ransomware variants with varying pre-encryption phases. The proposed model can detect the evolving ransomware variants by comprehensively generalizing significant attack patterns. Full article
(This article belongs to the Special Issue Research and Advances in Network Security)
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