Al-Enabled Secure Digital Identity Management in IoT Ecosystems: A comprehensive Survey

Identity management is a fundamental requirement in modern Internet of Things (IoT) ecosystems, where large-scale, heterogeneous devices continuously exchange sensitive data across distributed and resource-constrained environments. As IoT deployments expand, ensuring secure authentication, authorization, and lifecycle management of devices becomes increasingly complex. IoT systems are particularly vulnerable to identity spoofing, unauthorized access, data manipulation, and large-scale cyber-attacks due to weak credentials, scalability challenges, and decentralized architectures. Artificial Intelligence (AI) has emerged as a key enabler of secure digital identities by supporting adaptive authentication, behavioral analysis, anomaly detection, risk-aware access control, and threat prediction. This survey synthesizes findings from ۴۰ recent studies exploring the convergence of AI, blockchain, federated learning, decentralized identity models, and zero-trust architectures for IoT identity management. A structured taxonomy of AI techniques for identity verification, risk assessment, and access control is presented, followed by a comparative analysis of existing frameworks with respect to scalability, interoperability, trust, privacy preservation, and computational efficiency. The survey further identifies open research challenges and outlines future directions toward lightweight, privacy-preserving, and interoperable AI-enabled identity management frameworks for large-scale IoT deployments.

کلیدواژه ها:

Digital Identity Management ، IoT Security ، Artificial Intelligence ، Authentication ، Federated Learning