Designing Short-Stage CDC-XPUFs: Balancing Reliability, Cost, and Security in IoT Devices

TL;DR

This work proposes an optimized CDC-XPUF design that incorporates a pre-selection strategy to enhance reliability and introduces a novel lightweight architecture to reduce hardware overhead, and demonstrates that the design significantly lowers resource consumption, maintains strong resistance to ML attacks, and improves reliability.

Abstract

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.

Figures (3)

• Figure 1: An arbiter PUF with n bits of challenge
• Figure 2: An XOR-PUF with 3 sub-stream and n bits of each stream
• Figure 3: Implementation of the pre-selection strategy in an Arbiter PUF, illustrating the conditional path adjustments for enhancing response reliability.