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Electronics
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Blockchain and Artificial Intelligence for Next-Generation Network and Cybersecurity
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Blockchain and Artificial Intelligence for Next-Generation Network and Cybersecurity

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 November 2026 | Viewed by 1050

Special Issue Editors

Dr. Guangsheng Yu

E-Mail Website
Guest Editor
School of Electrical and Data Engineering, University of Technology Sydney, 15 Broadway, Ultimo, Sydney, NSW 2007, Australia
Interests: cybersecurity in AI; responsible AI; AI robustness and fairness; distributed learning; distributed ledger technology; blockchain; Web3.0
Dr. Xu Wang

E-Mail Website
Guest Editor
School of Electrical and Data Engineering, University of Technology Sydney, 15 Broadway, Ultimo, Sydney, NSW 2007, Australia
Interests: cybersecurity; blockchain; privacy; network dynamics
Special Issues, Collections and Topics in MDPI journals
Dr. Qin Wang

E-Mail Website
Guest Editor
CSIRO Data61, Sydney, Australia
Interests: Web3; blockchain; distributed ledger technology

Special Issue Information

Dear Colleagues,

This Special Issue on Blockchain and Artificial Intelligence for Next-Generation Network and Cybersecurity seeks to explore how the integration of blockchain and AI can provide robust, intelligent, and scalable solutions for the security and reliability of future communication networks. The focus is on advancing the state of knowledge at the intersection of these technologies to address pressing challenges in both networking and cybersecurity domains.

The scope covers a wide range of research directions. Relevant topics include blockchain protocols for trustworthy and auditable data exchange in 5G and 6G environments, AI-enhanced intrusion detection and anomaly detection in vehicular and edge networks, privacy-preserving federated learning for distributed network infrastructures, decentralized identity and trust management for IoT ecosystems, and adversarial robustness for intelligent network services. We also encourage contributions on governance frameworks, auditing tools, and compliance strategies that ensure practical and regulatory alignment, as well as cross-domain use cases involving smart cities, industrial IoT, and cyber–physical systems.

The purpose is to create a dedicated platform for researchers and practitioners to showcase new methods, frameworks, and applications that combine the decentralized trust of blockchain with the adaptive intelligence of AI. By bringing together advances in next-generation networks, cybersecurity, and privacy, this Special Issue will identify synergies, open challenges, and design patterns that can guide the development of trustworthy digital infrastructures at scale.

In relation to the broader literature, this Special Issue will supplement existing research by bridging work that has traditionally been siloed into separate streams—blockchain for secure networking, AI for network intrusion detection, and blockchain or AI for general cybersecurity. By highlighting their integration, this issue will provide insights into novel attack and defense surfaces, integrated architectures for secure and intelligent networking, and comprehensive approaches that go beyond single-technology perspectives. This integration is especially timely as the deployment of 6G, vehicular networks, and highly distributed IoT systems accelerates.

Dr. Guangsheng Yu
Dr. Xu Wang
Dr. Qin Wang
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics 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 2400 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

  • blockchain
  • artificial intelligence
  • next-generation networks
  • cybersecurity
  • privacy
  • 5G
  • 6G
  • vehicular networks
  • IoT
  • edge computing
  • trust management
  • adversarial robustness
  • privacy-preserving machine learning
  • secure data sharing

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

Published Papers (2 papers)

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24 pages, 790 KB  
Article
Maturity-Aware Cyber Insurance Optimization in IoT Networks
by Bishwa Bhusal, Delong Li, Xu Wang and Guangsheng Yu
Electronics 2026, 15(5), 1038; https://doi.org/10.3390/electronics15051038 - 2 Mar 2026
Viewed by 261
Abstract
As the rapid evolution and expansion of Internet of Things (IoT) devices continues to accelerate, modern infrastructures face increasing cyber risks, largely driven by device inter-connectivity, limited security maturity, and interdependent attack propagation across networks. Traditional cyber insurance models often overlook these IoT-specific [...] Read more.
As the rapid evolution and expansion of Internet of Things (IoT) devices continues to accelerate, modern infrastructures face increasing cyber risks, largely driven by device inter-connectivity, limited security maturity, and interdependent attack propagation across networks. Traditional cyber insurance models often overlook these IoT-specific characteristics, relying on uniform or simplified risk assumptions that fail to capture real-world vulnerabilities. To address this gap, this paper presents a maturity-aware cyber insurance optimization framework tailored for interconnected IoT environments. The framework integrates organizational security maturity, interdependent risk propagation modeled through a modified Susceptible–Infected–Susceptible (SIS) process, and a Stackelberg game formulation that captures strategic interactions between the insurer and the defender. Through numerical studies on representative IoT topologies, we demonstrate that maturity-aware, risk-sensitive premium structures quantitatively outperform uniform pricing baselines in cost-efficiency and insurer sustainability. Specifically, our experimental results reveal that operating at an optimal intermediate maturity level (M=3) reduces the defender’s total expected cost by approximately 40% (from 255.38 k to 152.36 k) compared to the baseline state (M=1). Furthermore, this structural hardening triggers an 88.3% reduction in full-coverage insurance premiums (from 225.38 k to 26.36 k). In contrast, our uniform-pricing baseline exhibits reduced profitability in our experiments due to cross-subsidization effects, reinforcing the value of tiered, risk-proportional pricing for mitigating adverse-selection incentives. In summary, this work establishes a tractable, economically viable framework for cyber insurance in IoT ecosystems and provides a foundation for future extensions to richer network settings. Full article
(This article belongs to the Special Issue Blockchain and Artificial Intelligence for Next-Generation Network and Cybersecurity)
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25 pages, 10798 KB  
Article
BERTSC: A Multi-Modal Fusion Framework for Stablecoin Phishing Detection Based on Graph Convolutional Networks and Soft Prompt Encoding
by Weixin Xie, Qihao Chen, Kexin Zhu, Chen Feng and Zhide Chen
Electronics 2026, 15(1), 179; https://doi.org/10.3390/electronics15010179 - 30 Dec 2025
Viewed by 472
Abstract
As stablecoins become increasingly prevalent in financial crimes, their usage for illicit activities has reached a scale of USD 51.3 billion. Detecting phishing activities within stablecoin transactions has emerged as a critical challenge in blockchain security. Currently, existing detection methods predominantly target mainstream [...] Read more.
As stablecoins become increasingly prevalent in financial crimes, their usage for illicit activities has reached a scale of USD 51.3 billion. Detecting phishing activities within stablecoin transactions has emerged as a critical challenge in blockchain security. Currently, existing detection methods predominantly target mainstream cryptocurrencies like Ethereum and lack specialized models tailored to the unique transaction patterns of stablecoin networks. This paper introduces a deep learning framework, BERTSC, based on multi-modal fusion. The model integrates three core modules graph convolutional networks (GCNs), BERT semantic encoders, and soft prompt encoders to identify malicious accounts. The GCN constructs directed multi-graph representations of account interactions, incorporating multi-dimensional edge features; the BERT encoder transforms discrete transaction attributes into semantically rich continuous vector representations; the soft prompt encoder maps account interaction features into learnable prompt vectors. An innovative three-way gated dynamic fusion mechanism optimally combines the information from these sources. The fused features are then classified to predict phishing account labels, facilitating the detection of phishing scams in stablecoin transaction datasets. Experimental results on large-scale stablecoin datasets demonstrate that BERTSC outperforms baseline models, achieving improvements of 4.96%, 3.60%, and 4.23% in Precision, Recall, and F1-score, respectively. Ablation studies validate the effectiveness of each module and confirm the necessity and superiority of the three-way gating fusion mechanism. This research offers a novel technical approach for phishing detection within blockchain stablecoin ecosystems. Full article
(This article belongs to the Special Issue Blockchain and Artificial Intelligence for Next-Generation Network and Cybersecurity)
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