Analysis of authentication mechanisms and identity management in IoT: the resource constraints and security needs

With an emphasis on identifying and reducing deepfake risks in digital identity verification, this essay examines the crucial nexus between artificial intelligence and authentication systems. The article offers a thorough framework that keeps strong security measures in place while addressing the changing difficulties posed by synthetic media attacks. The article explores how behavioral biometrics and AI-driven verification layers can improve conventional techniques through an investigation of multi-modal authentication systems. The use of federated learning and cutting-edge privacy-preserving technologies to safeguard private information while preserving system performance is examined in this paper. The methodology ensures responsible AI implementation in authentication systems by incorporating ethical considerations and regulatory compliance requirements. Through an analysis of performance indicators and real-world deployments, this article offers insights into resource utilization techniques, maintenance procedures, and system optimization. The results give enterprises a road map for putting in place robust identity verification systems in a time of more complex digital impersonation efforts by illustrating how AI may improve authentication procedures while addressing privacy issues and new security threats.

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Analysis of authentication mechanisms and identity management in IoT: the resource constraints and security needs

Cited by 3 publications

References 1 publication

A Systematic Literature Mapping on Using Blockchain Technology in Identity Management

A Systematic Literature Mapping on Using Blockchain Technology in Identity Management

Security and Trust Issues in BYOD Networks

Advanced AI-Based Authentication Framework: Detecting and Mitigating Deepfake Threats in Digital Identity Systems

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