A Unified Open-Set Framework for Scalable PUF-Based Authentication of Heterogeneous IoT Devices

• URL: http://arxiv.org/abs/2605.07340v1
• Date: Fri, 08 May 2026 06:46:41 GMT
• Title: A Unified Open-Set Framework for Scalable PUF-Based Authentication of Heterogeneous IoT Devices
• Abstract summary: This paper proposes a scalable, helper-data-free, open-set PUF authentication framework to manage heterogeneous fleets of IoT devices.<n>Our framework achieves 100% closed-set accuracy and near-zero open-set error rates with up to 45 devices.<n> Prototyped on a Raspberry Pi, our framework completes one authentication cycle within 0.67 s, approximately 30x faster than the state-of-the-art open-set baselines.
• Score: 13.17300268210473
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As modern cyber systems scale to include large populations of heterogeneous IoT devices, securing them against impersonation and forgery is a critical cybersecurity challenge. Physical Unclonable Functions (PUFs) offer a lightweight, hardware-rooted trust anchor for IoT security. However, different PUF architectures possess distinct challenge-response spaces and raw response reliabilities, making existing authentication protocols PUF-type specific. To bridge this interoperability bottleneck, this paper proposes a scalable, helper-data-free, open-set PUF authentication framework that leverages an OpenGAN-based classifier to manage heterogeneous fleets of IoT devices. Our method addresses the limitations of traditional database-centric and digital-twin modeling methods by encoding raw responses from diverse PUF types, including strong, weak and hybrid PUFs, into a unified image representation. This enables robust, single-pass classification and impostor rejection. We integrate the classifier into a generic protocol employing hybrid encryption and Bloom filter-based replay detection. Evaluated across four different types of noisy PUF data (Arbiter, SRAM, DRAM, and heterogeneous PUFs), our framework achieves 100% closed-set accuracy and near-zero open-set error rates with up to 45 devices, a significant improvement over the 3 to 5 devices in prior classification-based approaches. Prototyped on a Raspberry Pi, our framework completes one authentication cycle within 0.67 s, approximately 30x faster than the state-of-the-art open-set baselines.

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