提出新型轻量级硬件安全模块，兼顾可靠、低成本与防攻击能力。
Designing Short-Stage CDC-XPUFs: Balancing Reliability, Cost, and Security in IoT Devices
- 采用预筛选策略提升可靠性，设计轻量架构降低资源开销。
- 实验表明其硬件成本显著降低，且有效抵御机器学习攻击。
- 适合对安全性与资源敏感的物联网设备部署。
物联网设备的快速扩张亟需高效可靠的安全部署方案。物理不可克隆函数（PUFs）通过利用硬件固有差异生成唯一密钥，是极具前景的解决方案。然而传统PUF如仲裁型PUF（APUF）和异或仲裁型PUF（XOR-PUF）易受机器学习及可靠性攻击。本文研究了一种较少探索的组件差分挑战异或型PUF（CDC-XPUF），提出优化设计：引入预筛选策略以增强可靠性，并开发新型轻量级架构以减少硬件开销。大量测试表明，该设计显著降低资源消耗，保持强机器学习抗性，同时提升可靠性，有效缓解可靠性攻击问题。结果证明CDC-XPUF在资源受限的物联网系统中具备广泛应用潜力。
原文摘要 · Abstract (English)
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.
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