Tu Dac Ho

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.

Dao Lan Vy Dinh, Anh Nguyen Thi Mai, Hung Tran et al.

This paper investigates the unmanned aerial vehicle (UAV)-assisted resilience perspective in the 6G network energy saving (NES) scenario. More specifically, we consider multiple ground base stations (GBSs) and each GBS has three different sectors/cells in the terrestrial networks, and multiple cells are turned off due to NES or incidents, e.g., disasters, hardware failures, or outages. To address this, we propose a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to enable UAV-assisted communication by jointly optimizing UAV trajectories, transmission power, and user-UAV association under a sleeping ground base station (GBS) strategy. This framework aims to ensure the resilience of active users in the network and the long-term operability of UAVs. Specifically, it maximizes service coverage for users during power outages or NES zones, while minimizing the energy consumption of UAVs. Simulation results demonstrate that the proposed MADDPG policy consistently achieves high coverage ratio across different testing episodes, outperforming other baselines. Moreover, the MADDPG framework attains the lowest total energy consumption, with a reduction of approximately 24\% compared to the conventional all GBS ON configuration, while maintaining a comparable user service rate. These results confirm the effectiveness of the proposed approach in achieving a superior trade-off between energy efficiency and service performance, supporting the development of sustainable and resilient UAV-assisted cellular networks.