Hossien B. Eldeeb

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.

Brahim Mefgouda, Raviha Khan, Omar Alhussein et al.

By 2025, the internet of things (IoT) is projected to connect over 75 billion devices globally, fundamentally altering how we interact with our environments in both urban and rural settings. However, IoT device security remains challenging, particularly in the authentication process. Traditional cryptographic methods often struggle with the constraints of IoT devices, such as limited computational power and storage. This paper considers physical unclonable functions (PUFs) as robust security solutions, utilizing their inherent physical uniqueness to authenticate devices securely. However, traditional PUF systems are vulnerable to machine learning (ML) attacks and burdened by large datasets. Our proposed solution introduces a lightweight PUF mechanism, called LPUF-AuthNet, combining tandem neural networks (TNN) with a split learning (SL) paradigm. The proposed approach provides scalability, supports mutual authentication, and enhances security by resisting various types of attacks, paving the way for secure integration into future 6G technologies.