2024/09/26 by Gaoxiang Li, Yu Zhuang, Li, Gaoxiang +1
Engineering · Physics and Astronomy · #Advanced Optical Sensing Technologies #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Integrated Circuits and Semiconductor Failure Analysis #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2409.17902
openalex publication_date 2024/09/26 · openalex created_date 2024/10/27 · openalex updated_date 2026/07/28
The rapid expansion of Internet of Things (IoT) devices demands robust and resource-efficient security solutions. Physically Unclonable Functions (PUFs), which generate unique cryptographic keys from inherent hardware variations, offer a promising approach. However, traditional PUFs like Arbiter PUFs (APUFs) and XOR Arbiter PUFs (XOR-PUFs) are susceptible to machine learning (ML) and reliability-based attacks. In this study, we investigate Component-Differentially Challenged XOR-PUFs (CDC-XPUFs), a less explored variant, to address these vulnerabilities. We propose an optimized CDC-XPUF design that incorporates a pre-selection strategy to enhance reliability and introduces a novel lightweight architecture to reduce hardware overhead. Rigorous testing demonstrates that our design significantly lowers resource consumption, maintains strong resistance to ML attacks, and improves reliability, effectively mitigating reliability-based attacks. These results highlight the potential of CDC-XPUFs as a secure and efficient candidate for widespread deployment in resource-constrained IoT systems.