Blockchain and AI: Securing Intelligent Networks for the Future

2026-04-07

AI Summary

This work addresses the growing complexity of cybersecurity threats in the era of ubiquitous interconnectivity by proposing a novel security framework that uniquely integrates blockchain and artificial intelligence to achieve trustworthiness, resilience, and proactive defense. The proposed architecture leverages blockchain for immutable data provenance and trust establishment, while employing AI for anomaly detection and threat prediction. Furthermore, it incorporates large language models to drive automated security workflows—such as alert triage and adaptive policy response—enabling intelligent, self-adaptive protection mechanisms. Empirical evaluations across Internet of Things, vehicular networks, and smart healthcare scenarios demonstrate that the framework significantly enhances system resilience, improves threat detection accuracy, and enables trustworthy autonomous security operations.

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 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.

Problem

• Intelligent Networks
• Cyber Threats
• Internet of Everything
• Security
• Critical Infrastructure

Innovation

• Blockchain-AI integration
• Agentic AI
• Threat intelligence
• Adaptive security
• Cyber-physical systems