Next-Generation Cybersecurity Intelligence: AI-Driven Defense, Quantum-Safe Systems, and Resilient Digital and Environmental Ecosystems

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 3480

Special Issue Editor


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Department of Information Systems and Technology (IS&T), The Smith College of Engineering and Technology (SCET), Utah Valley University (UVU), 800 W University Pkwy, Orem, UT 84058, USA
Interests: software design and modeling; data analysis; web technology; security analysis; AI; machine learning; cloud computing
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Special Issue Information

Dear Colleagues,

The digital, physical, and environmental worlds are being increasingly interwoven, exposing cyber–physical systems and data ecosystems to rapidly evolving threats, ranging from adversarial AI to the coming disruption of large‑scale quantum computing. This Special Issue aims to advance the state of practice and science in the field of cybersecurity by bringing together AI‑driven defense, quantum‑safe cryptography, and systems‑level resilience across hyperconnected infrastructures. We seek rigorous research that combines learning‑based intelligence (e.g., LLMs, diffusion models) with principled security engineering (zero‑trust, formal methods, verifiable cryptography), as well as studies that integrate human factors, governance, and ethics.

The Systems journal emphasizes holistic, cross‑disciplinary thinking about complex adaptive systems. This Special Issue frames cybersecurity as a multi‑scale systems problem—spanning algorithms, socio‑technical processes, and environmental contexts—prioritizing reproducible methods, open benchmarks, and end‑to‑end evaluation in real deployments. We welcome papers that include theory, methods, datasets, and applications used to model, analyze, and harden complex digital ecosystems.

Topics of interest include, but are not limited to, the following:

  • Intelligent and autonomous security systems;
  • Generative AI and LLM security;
  • Post‑quantum cryptography and quantum‑safe systems;
  • Emerging technology security (e.g., edge/5G, XR, AR/VR);
  • Resilient cyber–physical systems and environmental protection;
  • Modeling digital ecosystems as complex adaptive systems;
  • Zero‑trust and supply chain security;
  • Privacy and ethics in hyperconnected ecosystems;
  • Multi-modal and multi‑domain datasets and benchmarks;
  • Diffusion models (security, robustness, evaluation);
  • Cryptography, encryption, and key‑management techniques;
  • Federated learning (privacy‑preserving, secure aggregation);
  • 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, 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) for cyber‑threat intelligence;
  • Secure and privacy‑preserving LLM architectures and training methods;
  • Ethical considerations and 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 ML‑based cybersecurity systems.

Dr. Anas M. Alsobeh
Guest Editor

Manuscript Submission Information

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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.

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Keywords

  • AI driven cybersecurity
  • LLMs and generative AI security
  • quantum safe/post quantum cryptography
  • zero trust architectures
  • adversarial machine learning and robustness
  • cyber–physical systems resilience
  • IoT and edge security
  • privacy, ethics, and governance
  • threat intelligence and benchmarks
  • secure software engineering and supply chains

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Published Papers (2 papers)

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Research

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34 pages, 1621 KB  
Article
Zero-Knowledge-Based Policy Enforcement for Privacy-Preserving Cross-Institutional Health Data Sharing on Blockchain
by Faisal Albalwy
Systems 2026, 14(4), 385; https://doi.org/10.3390/systems14040385 - 2 Apr 2026
Viewed by 1088
Abstract
This study presents ZK-EHR, a decentralized access control framework designed to enable secure and privacy-preserving sharing of encrypted electronic health records across institutional boundaries. Unlike existing blockchain-based EHR access control systems that expose user identities on-chain or lack cryptographic privacy guarantees, ZK-EHR decouples [...] Read more.
This study presents ZK-EHR, a decentralized access control framework designed to enable secure and privacy-preserving sharing of encrypted electronic health records across institutional boundaries. Unlike existing blockchain-based EHR access control systems that expose user identities on-chain or lack cryptographic privacy guarantees, ZK-EHR decouples authorization from identity disclosure by integrating zk-SNARK-based proofs with blockchain smart contracts to verify policy compliance without revealing user roles, affiliations, or credentials. The framework employs three differentiated actor roles—Patient (Data Owner), Doctor (Care Provider), and Researcher (Authorized Analyst)—with distinct policy-driven access workflows, a custom Groth16 zero-knowledge circuit for role-based constraint enforcement, and a modular architecture combining on-chain verification with off-chain encrypted storage via IPFS. Concrete design proposals for access revocation and replay attack prevention are introduced to address operational security requirements. The system was evaluated under multiple operational and adversarial scenarios. Experimental results indicate consistent on-chain verification latency (approximately 390 ms), reliable rejection of tampered submissions, and per-verification gas consumption of 216,631 gas. A comparative analysis against representative baseline systems demonstrates that ZK-EHR uniquely combines identity anonymity, on-chain cryptographic policy enforcement, and auditable encrypted record retrieval. These findings establish the feasibility of zk-SNARK-based access control for decentralized, verifiable, and privacy-aware EHR management. Full article
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Review

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21 pages, 1162 KB  
Review
Machine Learning Based Spam Detection in Digital Communication Systems: A Comparative Analysis
by Maram Bani Younes and Ahmad Ababneh
Systems 2026, 14(3), 229; https://doi.org/10.3390/systems14030229 - 24 Feb 2026
Viewed by 1888
Abstract
Spam messages are unwanted, irrelevant, or potentially harmful messages sent in bulk to large numbers of recipients via email, SMS, or social media. These messages pose a threat of spam to individual users and commercial companies. They threaten digital communication platforms by enabling [...] Read more.
Spam messages are unwanted, irrelevant, or potentially harmful messages sent in bulk to large numbers of recipients via email, SMS, or social media. These messages pose a threat of spam to individual users and commercial companies. They threaten digital communication platforms by enabling phishing, malware distribution, service disruption, and unsolicited advertisements. Several mechanisms have been used in the literature to detect spam over digital communication systems. This includes rule-based filtering, Bayesian filtering, heuristic analysis, and machine learning (ML) techniques. Traditional rule-based and heuristic analyses were insufficient to cope with evolving attack patterns. Meanwhile, ML models can present modern, dynamic, appropriate, and efficient solutions in this manner. This study aims to evaluate and compare several basic ML models for spam detection, considering popular benchmark datasets on several communication platforms as a comprehensive comparative study. The experimental results demonstrate that the tested models achieve good accuracy, precision, recall, and F1-score on each investigated benchmark dataset. However, the performance of all models has decreased drastically when the trained models are tested on an unseen dataset. Recommendations for future required enhancements to handle this reduction in the performance of ML techniques for unseen datasets are provided. Finally, extra experimental tests have shown the positive impact of applying some of these recommendations. Full article
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