AI-Powered Trust Management and Cryptographic Access Control in IoT Systems

Abstract

The rapid proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented connectivity and innovation across various sectors such as healthcare, smart cities, and industrial automation. However, this pervasive integration also introduces significant security, privacy, and trust challenges, primarily due to the decentralized, dynamic, and resource-constrained nature of IoT ecosystems. Ensuring secure and reliable interactions among heterogeneous devices and users is paramount for the successful deployment and adoption of IoT technologies. This paper comprehensively reviews AI-powered trust management and cryptographic access control mechanisms designed to address these critical issues in IoT systems. We explore the fundamental principles, advanced techniques, and the synergistic integration of AI and cryptography to enhance security, privacy, and trustworthiness. Drawing upon recent research from 2023 to 2026, we analyze the strengths, limitations, and practical implications of various solutions, including AI-driven anomaly detection, federated learning, homomorphic encryption, and blockchain-based trust models. Furthermore, we identify emerging trends, future directions, and persistent challenges such as scalability, interoperability, and ethical considerations, offering a holistic perspective on the evolving landscape of secure IoT. The aim is to synthesize current knowledge and highlight key research avenues for developing more robust, adaptive, and context-aware security frameworks for future IoT environments.