A Unified Open-Set Framework for Scalable PUF-Based Authentication of Heterogeneous IoT Devices
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915991891476480 |
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| author | Wang, Xin Hua, Peichun Chang, Chip Hong Liu, Wenye Zheng, Yue |
| author_facet | Wang, Xin Hua, Peichun Chang, Chip Hong Liu, Wenye Zheng, Yue |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_07340 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Unified Open-Set Framework for Scalable PUF-Based Authentication of Heterogeneous IoT Devices Wang, Xin Hua, Peichun Chang, Chip Hong Liu, Wenye Zheng, Yue Cryptography and Security 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. |
| title | A Unified Open-Set Framework for Scalable PUF-Based Authentication of Heterogeneous IoT Devices |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2605.07340 |