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Blockchain and AI: Securing Intelligent Networks for the Future

arXiv Security Archived Apr 09, 2026 ✓ Full text saved

arXiv:2604.06323v1 Announce Type: new Abstract: The rapid evolution of intelligent networks under the Internet of Everything (IoE) paradigm is transforming connectivity by integrating people, processes, data, and things. This ecosystem includes domains such as the Internet of Things (IoT), Internet of Healthcare (IoH), Internet of Vehicles (IoV), and cyber-physical and human-machine systems. While enabling efficiency and automation, this interconnectivity also exposes critical infrastructures to

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    Computer Science > Cryptography and Security [Submitted on 7 Apr 2026] Blockchain and AI: Securing Intelligent Networks for the Future Joy Dutta, Hossien B. Eldeeb, Tu Dac Ho The rapid evolution of intelligent networks under the Internet of Everything (IoE) paradigm is transforming connectivity by integrating people, processes, data, and things. This ecosystem includes domains such as the Internet of Things (IoT), Internet of Healthcare (IoH), Internet of Vehicles (IoV), and cyber-physical and human-machine systems. While enabling efficiency and automation, this interconnectivity also exposes critical infrastructures to increasingly sophisticated cyber threats, creating an urgent need for advanced security solutions. This chapter examines the integration of Blockchain and Artificial Intelligence (AI) as complementary approaches for securing intelligent networks. Blockchain provides decentralized, immutable, and transparent mechanisms that strengthen data integrity, trust, and accountability. In parallel, AI offers predictive analytics, anomaly detection, and adaptive defense capabilities to enable proactive threat identification and mitigation. The chapter discusses how Blockchain supports security in cyber-physical systems, how AI enables proactive security operations, and how their combination creates robust, adaptive, and trustworthy security frameworks. The chapter also explores the emerging role of large language models in threat intelligence and analyzes how controlled agentic AI can support bounded security workflows such as alert triage, evidence collection, and policy-aware response planning. Representative case studies illustrate the potential of these technologies to enhance cyber resilience. Finally, challenges related to scalability, energy efficiency, and ethical considerations are addressed, along with reported mitigation strategies and future research directions. Overall, this chapter provides researchers, practitioners, and policymakers with insights to design secure, resilient, and adaptable intelligent networks. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.06323 [cs.CR]   (or arXiv:2604.06323v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.06323 Focus to learn more Submission history From: Tu Dac Ho [view email] [v1] Tue, 7 Apr 2026 18:00:40 UTC (9,844 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Apr 09, 2026
    Archived
    Apr 09, 2026
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