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Mathematics
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Analytical Frameworks and Methods for Cybersecurity, 2nd Edition
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Analytical Frameworks and Methods for Cybersecurity, 2nd Edition

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 March 2026 | Viewed by 2199

Special Issue Editor

Prof. Dr. Todor Tagarev

E-Mail Website
Guest Editor
Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
Interests: OR; complexity; big data; cybersecurity; cyber defence; crisis management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Mathematics Special Issue on “Analytical Frameworks and Methods for Cybersecurity, 2nd Edition”.

Critical infrastructures, the provision of essential services as well as individual and group perceptions are increasingly under sophisticated attack through cyberspace. The application of adequate frameworks and advanced analytical methods can increase the effectiveness of mitigation and protection measures as well as the response to cyberattacks. This Special Issue is dedicated to rigorous analytics including, but not limited to, deep learning over big data to model attacks, providing situational awareness, detecting anomalies, classifying intrusion attempts, coordinating the response, optimizing resilience measures, protecting information and communications, detecting and designing countermeasures for malign online influence and disinformation, and minimizing the vulnerabilities of network and information systems and supply chains.

Prof. Dr. Todor Tagarev
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

  • Cybersecurity
  • Cyber–physical systems
  • Cyber persona
  • Influence operations
  • Attack modelling
  • Situational awareness
  • Intrusion detection
  • Classification
  • Forensics
  • Risk management
  • Resilience
  • Coding
  • Cryptography
  • Artificial intelligence
  • Deep learning
 
 

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Published Papers (2 papers)

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Research

22 pages, 311 KB  
Article
A Dempster–Shafer, Fusion-Based Approach for Malware Detection
by Patricio Galdames, Simon Yusuf Enoch, Claudio Gutiérrez-Soto and Marco A. Palomino
Mathematics 2025, 13(16), 2677; https://doi.org/10.3390/math13162677 - 20 Aug 2025
Viewed by 373
Abstract
Dempster–Shafer theory (DST), a generalization of probability theory, is well suited for managing uncertainty and integrating information from diverse sources. In recent years, DST has gained attention in cybersecurity research. However, despite the growing interest, there is still a lack of systematic comparisons [...] Read more.
Dempster–Shafer theory (DST), a generalization of probability theory, is well suited for managing uncertainty and integrating information from diverse sources. In recent years, DST has gained attention in cybersecurity research. However, despite the growing interest, there is still a lack of systematic comparisons of DST implementation strategies for malware detection. In this paper, we present a comprehensive evaluation of DST-based ensemble mechanisms for malware detection, addressing critical methodological questions regarding optimal mass function construction and combination rules. Our systematic analysis was tested with 630,504 benign and malicious samples collected from four public datasets (BODMAS, DREBIN, AndroZoo, and BMPD) to train malware detection models. We explored three approaches for converting classifier outputs into probability mass functions: global confidence using fixed values derived from performance metrics, class-specific confidence with separate values for each class, and computationally optimized confidence values. The results establish that all approaches yield comparable performance, although fixed values offer significant computational and interpretability advantages. Additionally, we introduced a novel linear combination rule for evidence fusion, which delivers results on par with conventional DST rules while enhancing interpretability. Our experiments show consistently low false positive rates—ranging from 0.16% to 3.19%. This comprehensive study provides the first systematic methodology comparison for DST-based malware detection, establishing evidence-based guidelines for practitioners on optimal implementation strategies. Full article
(This article belongs to the Special Issue Analytical Frameworks and Methods for Cybersecurity, 2nd Edition)
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27 pages, 5252 KB  
Article
Mathematical Modeling and Clustering Framework for Cyber Threat Analysis Across Industries
by Fahim Sufi and Musleh Alsulami
Mathematics 2025, 13(4), 655; https://doi.org/10.3390/math13040655 - 17 Feb 2025
Cited by 2 | Viewed by 1314
Abstract
The escalating prevalence of cyber threats across industries underscores the urgent need for robust analytical frameworks to understand their clustering, prevalence, and distribution. This study addresses the challenge of quantifying and analyzing relationships between 95 distinct cyberattack types and 29 industry sectors, leveraging [...] Read more.
The escalating prevalence of cyber threats across industries underscores the urgent need for robust analytical frameworks to understand their clustering, prevalence, and distribution. This study addresses the challenge of quantifying and analyzing relationships between 95 distinct cyberattack types and 29 industry sectors, leveraging a dataset of 9261 entries filtered from over 1 million news articles. Existing approaches often fail to capture nuanced patterns across such complex datasets, justifying the need for innovative methodologies. We present a rigorous mathematical framework integrating chi-square tests, Bayesian inference, Gaussian Mixture Models (GMMs), and Spectral Clustering. This framework identifies key patterns, such as 1150 Zero-Day Exploits clustered in the IT and Telecommunications sector, 732 Advanced Persistent Threats (APTs) in Government and Public Administration, and Malware with a posterior probability of 0.287 dominating the Healthcare sector. Temporal analyses reveal periodic spikes, such as in Zero-Day Exploits, and a persistent presence of Social Engineering Attacks, with 1397 occurrences across industries. These findings are quantified using significance scores (mean: 3.25 ± 0.7) and posterior probabilities, providing evidence for industry-specific vulnerabilities. This research offers actionable insights for policymakers, cybersecurity professionals, and organizational decision makers by equipping them with a data-driven understanding of sector-specific risks. The mathematical formulations are replicable and scalable, enabling organizations to allocate resources effectively and develop proactive defenses against emerging threats. By bridging mathematical theory to real-world cybersecurity challenges, this study delivers impactful contributions toward safeguarding critical infrastructure and digital assets. Full article
(This article belongs to the Special Issue Analytical Frameworks and Methods for Cybersecurity, 2nd Edition)
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