Blockchain for Cybersecurity and Cyber-Risk Management

A Special Issue of Journal of Cybersecurity and Privacy (ISSN 2624-800X) belonging to the section "Cryptography and Cryptology".

Deadline for manuscript submissions: 1 November 2026 | Viewed by 4228

Editors


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Guest Editor
1. Cyber Security Research Centre, London Metropolitan University, London N7 8DB, UK
2. Cybersecurity Institute, University of Liverpool, Liverpool L69 3BX, UK
Interests: cyber security; digital forensics; Internet of Things; reinforcement learning; large language models; forensics; risk management and governance
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computer Science and Technology, Northumbria University, Newcastle NE1 2SU, UK
Interests: cutting-edge fields of cybersecurity; artificial intelligence, and machine learning, particularly on topics such as cyber-security and privacy, federated learning, image processing and computational

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Guest Editor
Department of Computer Science, The University of Jordan, Amman 11942, Jordan
Interests: cybersecurity; cyber-risk assessment; mobile apps

Special Issue Information

Dear Colleagues,

The primary aim of this Special Issue is to elevate the discourse surrounding cybersecurity by inspiring scientists to share their groundbreaking research and practical insights. Covering a wide array of topics, including blockchain, machine learning in security, artificial intelligence security, big data security and privacy, cloud security, and quantum security, it beckons academia, developers, policymakers, and cybersecurity analysts to contribute to the ongoing dialogue. By fostering collaboration and knowledge sharing, Risk Assessment and Countermeasures for Cybersecurity empowers readers not only to understand the nuances of modern cyber threats but also to actively participate in shaping the future of cybersecurity.

The interrelated topics of this Special Issue

Topic 1: Risk Assessment and Mitigation Strategies 

This topic will cover the principles, methods, and best practices involved in effective risk assessment and mitigation strategies. This chapter will focus on the cyber-risk management process and on how to employ various methods and tools to uncover cyber-risks across financial, operational, strategic, and compliance domains. In addition, the chapter will present the main proposed methods for identifying potential risks, analyzing and evaluating them to understand their likelihood and potential impact.  This involves assessing the probability of occurrence and the magnitude of consequences associated with each risk. 

Topic 2: Machine Intelligence Applications in Cyber-Risk Management 

Topic 2 explores the intersection of machine intelligence and cyber-risk management, highlighting the transformative impact of artificial intelligence (AI) and machine learning (ML) technologies in safeguarding against cyber threats. This chapter delves into various applications of machine intelligence in identifying, analyzing, and mitigating cyber risks, addressing the evolving landscape of cybersecurity threats faced by organizations globally. The core of the chapter focuses on exploring diverse applications of machine intelligence in cyber-risk management. This includes the use of AI and ML algorithms for real-time threat detection, anomaly detection, predictive analytics, and behavioral analysis. Additionally, machine intelligence technologies enable automated incident response, threat intelligence gathering, and adaptive security measures, empowering organizations to stay ahead of emerging cyber threats. 

Topic 3: Advanced Techniques for Network Security and Data Protection 

Topic 3 focuses on advanced techniques for enhancing network security and protecting sensitive data. This chapter explores innovative approaches and cutting-edge technologies designed to mitigate cybersecurity risks and safeguard organizational assets from evolving threats. The chapter emphasizes the critical importance of robust network security and data protection measures in safeguarding against cyber threats. It highlights the growing sophistication of cyberattacks and the need for organizations to adopt proactive strategies to defend their networks and sensitive data against unauthorized access, theft, and manipulation. The chapter discusses future directions in network security, such as the adoption of artificial intelligence, quantum-resistant encryption, and the proliferation of secure-by-design principles in network infrastructure development.

Topic 4: Blockchain for cyber-risk management 

Topic 4 discusses the innovative application of blockchain technology to cyber-risk management. This chapter explores how blockchain, originally developed as the underlying technology for cryptocurrencies, is being leveraged to address cybersecurity challenges, enhance data integrity, and strengthen risk management practices across industries. The chapter also addresses the challenges and considerations associated with implementing blockchain for cyber-risk management. These may include scalability limitations, interoperability issues, regulatory compliance, governance frameworks, and the need for robust cybersecurity measures to protect blockchain networks from cyber-attacks and vulnerabilities.

Dr. Mohamed Chahine Ghanem
Dr. Rejwan Bin Sulaiman
Dr. Mohammed Almaayah
Guest Editors

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Cybersecurity and Privacy 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 1200 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 risk assessment machine learning in security
  • artificial intelligence security
  • big data security and privacy
  • cloud security
  • digital and information forensics
  • quantum security
  • cyber–physical system security
  • network and mobile security
  • IoT security
  • security risk and engineering
  • management, policies, and human factors in security
  • privacy and cyber threat
  • anonymity and privacy
  • cryptography and cryptology
  • authentication and access control
  • biometrics
  • malware analysis
  • privacy-enhancing technologies and anonymity
  • IoT security
  • AI security

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Published Papers (5 papers)

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Research

20 pages, 2241 KB  
Article
Point Estimates Understate Quantum Theft Risk in Bitcoin: A Distributional Race Model for Commit–Delay–Reveal
by Suwichai Phunsa and Thawatchai Chomsiri
J. Cybersecur. Priv. 2026, 6(5), 162; https://doi.org/10.3390/jcp6050162 - 15 Sep 2026
Viewed by 162
Abstract
Published assessments of Bitcoin’s exposure to a cryptographically relevant quantum computer convert resource estimates into risk figures by substituting a point estimate of the key derivation time into an exponential tail. We show that this procedure is systematically optimistic. Because the exponential tail [...] Read more.
Published assessments of Bitcoin’s exposure to a cryptographically relevant quantum computer convert resource estimates into risk figures by substituting a point estimate of the key derivation time into an exponential tail. We show that this procedure is systematically optimistic. Because the exponential tail is strictly convex, its expectation over any non-degenerate break time distribution exceeds its value at the mean, so every such figure is a provable lower bound on the true risk: at an unchanged nine-minute mean, exponential dispersion moves Bitcoin’s on-spend theft probability from 41% to 53%. We develop the distributional model this requires, a race between a Poisson block-arrival process and a random time-to-key embedded in a Nakamoto reorganization contest and a replace-by-fee contest, and obtain closed forms for the theft probability, for the commit–reveal delay attaining a given security target, and for the coin value an owner retains in the fee war. Replacing the zero-delay catch-up bound with a delay-aware one raises the required delay by a factor of 1.2 to 20.4, a correction driven almost entirely by the adversary’s pre-mining lead rather than by propagation delay. Reconciling our results with a concurrent round-based analysis shows that an apparent threefold disagreement in the literature is a difference in security target, not in substance. Finally, we test the block-arrival assumption against 40,320 block headers: the exponential marginal law holds, but a conditional uniformity test detects within-epoch rate drift invisible to a Kolmogorov–Smirnov test, an effect worth under a third of a percentage point and again conservative. Full article
(This article belongs to the Special Issue Blockchain for Cybersecurity and Cyber-Risk Management)
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18 pages, 2769 KB  
Article
A Blockchain-Based System for Automating Secure Exchange of Birth Certificates
by Kaoutar Jouti, Manal Jlil and Chakir Loqman
J. Cybersecur. Priv. 2026, 6(5), 142; https://doi.org/10.3390/jcp6050142 - 24 Aug 2026
Viewed by 222
Abstract
The Moroccan Ministry of Justice aims to enhance the process of the judicial system. Through digitalization, given the sensitive information and the complexity of managing this volume of data, along with the multiple electronic materials exchanged, several challenges regarding the security, integrity, and [...] Read more.
The Moroccan Ministry of Justice aims to enhance the process of the judicial system. Through digitalization, given the sensitive information and the complexity of managing this volume of data, along with the multiple electronic materials exchanged, several challenges regarding the security, integrity, and confidentiality of personal data are presented that indicate difficulties in confirming authenticity. Using blockchain technology, the Ministry of Justice can exchange data and knowledge in a secure and transparent way. The goal of the proposed method is to automate the procedure for generating birth certificates to strengthen trust, security, and operational efficiency within the Moroccan judicial system. Full article
(This article belongs to the Special Issue Blockchain for Cybersecurity and Cyber-Risk Management)
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37 pages, 2657 KB  
Article
A Self-Healing Blockchain-Based Digital Twin Framework for Cybersecurity-Aware Fuzzy Multi-Objective Supply Chain Finance Optimization Under Uncertainty
by Hamed Nozari and Zornitsa Yordanova
J. Cybersecur. Priv. 2026, 6(4), 139; https://doi.org/10.3390/jcp6040139 - 18 Aug 2026
Viewed by 353
Abstract
The increasing dependence of financial supply chains on digital infrastructures has made it more necessary to design secure, resilient, and reliable networks than ever before. This research presents a self-healing framework based on blockchain and digital twins for multi-objective fuzzy optimization of financial [...] Read more.
The increasing dependence of financial supply chains on digital infrastructures has made it more necessary to design secure, resilient, and reliable networks than ever before. This research presents a self-healing framework based on blockchain and digital twins for multi-objective fuzzy optimization of financial supply chains under uncertainty. The proposed model, focusing on minimizing financial cost, cyber risk, and recovery time while simultaneously maximizing the level of trust and resilience, enables intelligent decision-making in the face of cyber threats. By combining real-time monitoring, secure transaction validation, fuzzy risk assessment, and automated recovery, the framework identifies the role of each component in maintaining the financial and operational stability of the network. The results showed that the complete model achieved an overall performance score of 0.944 in the component elimination study and increased the level of trust and resilience to 0.95 and 0.96, respectively. The cyber risk index was also maintained at 0.118, indicating the framework’s ability to control threats and maintain network stability. The findings show that the convergence of blockchain, digital twin, fuzzy logic, and self-healing mechanism can provide an effective basis for the development of smart, secure, and resilient financial supply chains. Full article
(This article belongs to the Special Issue Blockchain for Cybersecurity and Cyber-Risk Management)
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32 pages, 2557 KB  
Article
A Hybrid Blockchain-Based Framework for Adaptive Cyber-Risk Prediction and Multi-Layer Threat Mitigation in Enterprise Networks
by Udit Mamodiya, Indra Kishor, Rahat Naz, Mohammed Almaiah and Amer Alqutaish
J. Cybersecur. Priv. 2026, 6(3), 85; https://doi.org/10.3390/jcp6030085 - 6 May 2026
Cited by 1 | Viewed by 1435
Abstract
The environment of cybersecurity is changing at a higher rate than most automated defensive systems can keep pace with, and most enterprise-level solutions are based on a fixed set of rules or a black box with machine learning results. This leads to a [...] Read more.
The environment of cybersecurity is changing at a higher rate than most automated defensive systems can keep pace with, and most enterprise-level solutions are based on a fixed set of rules or a black box with machine learning results. This leads to a loophole between identifying and controlling responses, particularly where the mitigation should demand accountability, proportionality, and justifiable reliability. Current AI–blockchain models enhance logging and detection and are seldom used to enforce adaptive, understandable, or risk-weighted response automation. It presents AGML, a hybrid governance-based defense framework that integrates blockchain mitigation execution with reinforcement-tuned prediction of cyber-risks. The system scores the risk continuously, mitigates severity depending on the situation, and recalculates behavior via a closed feedback mechanism. The blockchain layer is an enforcement boundary and not a passive ledger as all activities are auditable and not tamperable. The results of the evaluation show that there is a quantifiable increase in comparison with recent baselines: 96.48% detection accuracy, 95.22% precision, 94.65% recall, and a false-positive rate of 2.81. The average response latency was 312 ms and around 26 ms was due to governance validation. The system was also found to be stable in repeated adversarial cycles and exhibited stable convergence as opposed to drifting. These findings indicate that responsible and responsive automation, not rapid but uninhibited automation, could provide a more feasible solution to the resilient enterprise cybersecurity. Full article
(This article belongs to the Special Issue Blockchain for Cybersecurity and Cyber-Risk Management)
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20 pages, 462 KB  
Article
The Evaluation of a Double-Spend Attack Probability for Ouroboros-like Proof-of-Stake Consensus
by Lyudmila Kovalchuk, Mariia Rodinko, Roman Oliynykov and Volodymyr Artemchuk
J. Cybersecur. Priv. 2026, 6(3), 80; https://doi.org/10.3390/jcp6030080 - 1 May 2026
Viewed by 1054
Abstract
This paper studies the probability of a double-spend attack in an Ouroboros-like Proof-of-Stake (PoS) setting when confirmation decisions must be made for a finite number of blocks. Existing security analyses of Ouroboros-family protocols are mainly asymptotic and therefore do not directly provide the [...] Read more.
This paper studies the probability of a double-spend attack in an Ouroboros-like Proof-of-Stake (PoS) setting when confirmation decisions must be made for a finite number of blocks. Existing security analyses of Ouroboros-family protocols are mainly asymptotic and therefore do not directly provide the attack probability for a fixed confirmation depth. We consider an analytically tractable model that allows empty slots and multiple slot leaders, and assumes fixed stake distribution within an epoch, one-block growth of the public longest chain in any slot containing at least one honest leader, and next-slot block visibility. These assumptions hold when the time slot length is much greater than the network delay, and are applicable to practical deployment scenarios such as Cardano. Under these assumptions, for the first time, an exact closed-form solution for the success probability of a double-spend attack considering a realistic model with multiple leaders and empty time slots. Numerical examples illustrate how the required confirmation depth depends on the adversarial stake ratio and the active slot coefficient. The results apply to the stated analytical model and do not yet cover delayed fork resolution or the full protocol-level fork-choice and finality mechanisms of Ouroboros Praos. Full article
(This article belongs to the Special Issue Blockchain for Cybersecurity and Cyber-Risk Management)
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