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Mathematics
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Cybersecurity and Data Protection: Modern Methods and New Applications
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Cybersecurity and Data Protection: Modern Methods and New Applications

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 August 2026 | Viewed by 4466

Special Issue Editors

Prof. Dr. Lukas Iffländer

E-Mail Website
Guest Editor
Faculty of Informatics/Mathematics, Dresden University of Applied Sciences, 01069 Dresden, Germany
Interests: network security; railway security; security and safety; quantum communication; encryption schemes; performance engineering
Thomas Prantl

E-Mail Website
Guest Editor Assistant
Mathematics and Computer Science, University of Würzburg, Würzburg, Germany
Interests: network security; security and safety; encryption schemes; performance engineering; homomorphic encryption

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue of the journal Mathematics entitled “Cybersecurity and Data Protection: Modern Methods and New Applications”. This initiative focuses on advances in mathematical research and practical applications in cybersecurity and data protection, which have been attracting growing interest due to their critical role in addressing contemporary cyber threats. Recent developments in applied mathematics have significantly contributed to enhancing the security and resilience of systems across various domains, including finance, healthcare, critical infrastructure, communication networks, and information systems. A wide range of complex challenges have been effectively tackled, such as cryptographic algorithm design, threat detection, risk assessment, secure data sharing, privacy preservation, and the optimization of security protocols.

This Special Issue invites high-quality original research or review papers on modern methods and new applications in cybersecurity and data protection to address the practical challenges in the related areas. The topics of interest include, but are not limited to, the following list:

  1. Cryptographic Methods Development of novel encryption algorithms
  2. Threat Detection and Prevention Mathematical models for identifying malware and phishing attacks
  3. Privacy Preservation Differential privacy and its applications
  4. Risk Assessment and Management Quantitative risk assessment models
  5. Secure Data Sharing and Storage Methods for ensuring data integrity and confidentiality
  6. Optimization of Security Protocols Game theory applications in cybersecurity
  7. Artificial Intelligence in Cybersecurity Machine learning models for cyber threat intelligence
  8. Mathematical Foundations of Cybersecurity Theoretical analysis of security models
  9. Applications in Industry Cybersecurity challenges in IoT and smart devices
  10. Emerging Areas and Trends Quantum computing implications for cybersecurity.

Prof. Dr. Lukas Iffländer
Guest Editor

Thomas Prantl
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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

  • advances in post-quantum cryptography
  • secure key generation and distribution techniques
  • intrusion detection systems using advanced analytics
  • anomaly detection in large-scale networks
  • secure multiparty computation techniques
  • homomorphic encryption for secure data processing
  • vulnerability analysis frameworks
  • optimization of risk mitigation strategies
  • methods for secure cloud storage
  • secure sharing protocols for big data applications
  • resource optimization in security systems
  • benchmarking of security system performance
  • deep learning techniques for real-time threat detection
  • explainable AI in cybersecurity applications
  • graph theory and network security applications
  • chaos theory in cryptographic systems
  • securing critical infrastructure systems
  • data protection in healthcare and financial services
  • cyber-physical systems security
  • ethical and legal considerations in data protection

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

Published Papers (4 papers)

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Research

37 pages, 624 KB  
Article
GeoVault: Leveraging Human Spatial Memory for Secure Cryptographic Key Management
by Marko Corn and Primož Podržaj
Mathematics 2026, 14(10), 1653; https://doi.org/10.3390/math14101653 - 13 May 2026
Viewed by 99
Abstract
Practical failures of cryptographic key management rarely stem from weak algorithms: they arise from the difficulty users face in memorizing and reliably recalling high-entropy secrets. Password-based and brainwallet approaches collapse under selection bias, while machine-generated mnemonics such as BIP-39 impose a significant memory [...] Read more.
Practical failures of cryptographic key management rarely stem from weak algorithms: they arise from the difficulty users face in memorizing and reliably recalling high-entropy secrets. Password-based and brainwallet approaches collapse under selection bias, while machine-generated mnemonics such as BIP-39 impose a significant memory burden. This paper introduces GeoVault, a key derivation framework that uses remembered geographic locations as the cryptographic input. Keys are derived from a small set of user-selected map points, encoded deterministically using a geospatial scheme and hardened with the Argon2id memory-hard function. We develop a formal entropy model that distinguishes nominal from effective spatial entropy under attacker-prioritized geographic dictionaries and quantifies the additional reduction caused by demographic selection bias. Through information-theoretic analysis and CPU-GPU benchmarking, we show that spatial secrets carry a substantially higher effective entropy floor than human-chosen passwords, and that Argon2id creates a strong asymmetry between legitimate users and offline adversaries: at a memory cost of 1 GiB, an attacker using a high-end GPU can test approximately 66 candidate secrets per one defender key derivation. This residual throughput advantage is, however, overwhelmed by the exponential growth of the search space when multiple locations are selected. Selecting n≥3 geographic points is necessary and sufficient to achieve cryptographic-strength brute-force resistance under the global attacker prior across all evaluated Argon2id configurations. Against a demographically targeted attacker with city-level knowledge of the user, n=3 maintains Human-Scale Secure resistance; n=4 with a chaining depth of k=6 restores the Super Secure zone at an ≈8 s user-side wait. Single-point configurations remain insecure regardless of memory cost hardening. Full article
(This article belongs to the Special Issue Cybersecurity and Data Protection: Modern Methods and New Applications)
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25 pages, 668 KB  
Article
A New Hybrid Method: CDRL-QNN for Stable IoT Intrusion Detection
by Muhammed Yusuf Küçükkara, Furkan Atban and Cüneyt Bayılmış
Mathematics 2026, 14(10), 1608; https://doi.org/10.3390/math14101608 - 9 May 2026
Viewed by 174
Abstract
The rapid expansion of the Internet of Things (IoT) has increased the risk of large-scale Distributed Denial-of-Service (DDoS) attacks. In high-availability IoT environments, the operational costs of false positives and false negatives are asymmetric, whereas conventional deep learning models usually optimize static accuracy-based [...] Read more.
The rapid expansion of the Internet of Things (IoT) has increased the risk of large-scale Distributed Denial-of-Service (DDoS) attacks. In high-availability IoT environments, the operational costs of false positives and false negatives are asymmetric, whereas conventional deep learning models usually optimize static accuracy-based objectives. To address this, we propose CDRL-QNN, a cost-aware and chaos-driven reinforcement learning quantum neural network framework in which a parameterized quantum circuit serves as the action-value function approximator within a Deep Q-Network (DQN) agent. The framework incorporates asymmetric operational penalties through both the reward function and sample-wise weighted Bellman optimization, while a logistic-map-based deterministic perturbation mechanism is used to promote exploration under constrained quantum-circuit training conditions. Evaluated on a computationally constrained balanced subset of the CIC-DDoS2019 dataset, the proposed framework reduced false negatives from 49 to 33 without increasing false positives, improving recall from 0.9673 to 0.9780 and F1-score from 0.9738 to 0.9793 while lowering operational cost. These findings suggest that hybrid quantum representations can be integrated into cost-sensitive reinforcement learning pipelines for IoT intrusion detection under constrained experimental conditions. Full article
(This article belongs to the Special Issue Cybersecurity and Data Protection: Modern Methods and New Applications)
24 pages, 502 KB  
Article
Exception-Driven Security: A Risk-Aware Permission Adjustment for High-Availability Embedded Systems
by Mina Soltani Siapoush and Jim Alves-Foss
Mathematics 2025, 13(20), 3304; https://doi.org/10.3390/math13203304 - 16 Oct 2025
Viewed by 946
Abstract
Real-time operating systems (RTOSs) are widely used in embedded systems to ensure deterministic task execution, predictable responses, and concurrent operations, which are crucial for time-sensitive applications. However, the growing complexity of embedded systems, increased network connectivity, and dynamic software updates significantly expand the [...] Read more.
Real-time operating systems (RTOSs) are widely used in embedded systems to ensure deterministic task execution, predictable responses, and concurrent operations, which are crucial for time-sensitive applications. However, the growing complexity of embedded systems, increased network connectivity, and dynamic software updates significantly expand the attack surface, exposing RTOSs to a variety of security threats, including memory corruption, privilege escalation, and side-channel attacks. Traditional security mechanisms often impose additional overhead that can compromise real-time guarantees. In this work, we present a Risk-aware Permission Adjustment (RPA) framework, implemented on CHERIoT RTOS, which is a CHERI-based operating system. RPA aims to detect anomalous behavior in real time, quantify security risks, and dynamically adjust permissions to mitigate potential threats. RPA maintains system continuity, enforces fine-grained access control, and progressively contains the impact of violations without interrupting critical operations. The framework was evaluated through targeted fault injection experiments, including 20 real-world CVEs and 15 abstract vulnerability classes, demonstrating its ability to mitigate both known and generalized attacks. Performance measurements indicate minimal runtime overhead while significantly reducing system downtime compared to conventional CHERIoT and FreeRTOS implementations. Full article
(This article belongs to the Special Issue Cybersecurity and Data Protection: Modern Methods and New Applications)
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25 pages, 1292 KB  
Article
Trust Domain Extensions Guest Fuzzing Framework for Security Vulnerability Detection
by Eran Dahan, Itzhak Aviv and Michael Kiperberg
Mathematics 2025, 13(11), 1879; https://doi.org/10.3390/math13111879 - 4 Jun 2025
Cited by 3 | Viewed by 2516
Abstract
The Intel® Trust Domain Extensions (TDX) encrypt guest memory and minimize host interactions to provide hardware-enforced isolation for sensitive virtual machines (VMs). Software vulnerabilities in the guest OS continue to pose a serious risk even as the TDX improves security against a [...] Read more.
The Intel® Trust Domain Extensions (TDX) encrypt guest memory and minimize host interactions to provide hardware-enforced isolation for sensitive virtual machines (VMs). Software vulnerabilities in the guest OS continue to pose a serious risk even as the TDX improves security against a malicious hypervisor. We suggest a comprehensive TDX Guest Fuzzing Framework that systematically explores the guest’s code paths handling untrusted inputs. Our method uses a customized coverage-guided fuzzer to target those pathways with random input mutations following integrating static analysis to identify possible attack surfaces, where the guest reads data from the host. To achieve high throughput, we also use snapshot-based virtual machine execution, which returns the guest to its pre-interaction state at the end of each fuzz iteration. We show how our framework reveals undiscovered vulnerabilities in device initialization procedures, hypercall error-handling, and random number seeding logic using a QEMU/KVM-based TDX emulator and a TDX-enabled Linux kernel. We demonstrate that a large number of vulnerabilities occur when developers implicitly rely on values supplied by a hypervisor rather than thoroughly verifying them. This study highlights the urgent need for ongoing, automated testing in private computing environments by connecting theoretical completeness arguments for coverage-guided fuzzing with real-world results on TDX-specific code. We discovered several memory corruption and concurrency weaknesses in the TDX guest OS through our coverage-guided fuzzing campaigns. These flaws ranged from nested #VE handler deadlocks to buffer overflows in paravirtual device initialization to faulty randomness-seeding logic. By exploiting these vulnerabilities, the TDX’s hardware-based memory isolation may be compromised or denial-of-service attacks may be made possible. Thus, our results demonstrate that, although the TDX offers a robust hardware barrier, comprehensive input validation and equally stringent software defenses are essential to preserving overall security. Full article
(This article belongs to the Special Issue Cybersecurity and Data Protection: Modern Methods and New Applications)
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