Physically Unclonable Functions for Secure IoT Authentication and Hardware-Anchored AI Model Integrity

• URL: http://arxiv.org/abs/2604.21188v1
• Date: Thu, 23 Apr 2026 01:06:42 GMT
• Title: Physically Unclonable Functions for Secure IoT Authentication and Hardware-Anchored AI Model Integrity
• Abstract summary: This survey reviews and synthesizes existing literature on hardware-rooted trust mechanisms for AI-enabled IoT systems.<n>It examines and compares representative trust anchor mechanisms, including TPM, PUFs, container-aware hardware roots of trust, and software-only security approaches.<n>It shows that PUF-based and hybrid trust anchors offer a promising balance for large-scale, AI-enabled IoT systems.
• Score: 1.9795169518658187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid integration of artificial intelligence (AI) into Internet of Things (IoT) and edge computing systems has intensified the need for robust, hardware-rooted trust mechanisms capable of ensuring device authenticity and AI model integrity under strict resource and security constraints. This survey reviews and synthesizes existing literature on hardware-rooted trust mechanisms for AI-enabled IoT systems. It systematically examines and compares representative trust anchor mechanisms, including Trusted Platform Module (TPM)-based measurement and attestation, silicon and FPGA-based Physical Unclonable Functions (PUFs), hybrid container-aware hardware roots of trust, and software-only security approaches. The analysis highlights how hardware-rooted solutions generally provide stronger protection against physical tampering and device cloning compared to software-only approaches, particularly in adversarial and physically exposed environments, while hybrid designs extend hardware trust into runtime and containerized environments commonly used in modern edge deployments. By evaluating trade-offs among security strength, scalability, cost, and deployment complexity, the study shows that PUF-based and hybrid trust anchors offer a promising balance for large-scale, AI-enabled IoT systems, whereas software-only trust mechanisms remain insufficient in adversarial and physically exposed settings. The presented comparison aims to clarify current design challenges and guide future development of trustworthy AI-enabled IoT platforms.

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