Robustness and Unpredictability for Double Arbiter PUFs on Silicon Data: Performance Evaluation and Modeling Accuracy
Robustness and Unpredictability for Double Arbiter PUFs on Silicon Data: Performance Evaluation and Modeling Accuracy
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DOI：
10.3390/electronics9050870
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发表时间：
2020-05
期刊：
影响因子：
2.9
通讯作者：
Meznah A. Alamro;Khalid T. Mursi;Yu Zhuang;Ahmad O. Aseeri;M. S. Alkatheiri
中科院分区：
文献类型：
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作者：
Meznah A. Alamro;Khalid T. Mursi;Yu Zhuang;Ahmad O. Aseeri;M. S. Alkatheiri
已知固有地采用嵌入在非易失性存储器中的秘密密钥的经典加密方法对于有限资源的物联网（IoT）设备是不切实际的。物理不可克隆功能（PUF）已经成为提供用于安全认证的无密钥手段的适用解决方案。PUF利用集成电路（IC）组件的不可避免的变化，在制造过程中显现，以提取独特的响应。最近提出了双仲裁器PUF（DAPUF）以克服XOR PUF中的安全问题并增强基于延迟的PUF对建模攻击的容忍度。本文提供了全面的风险分析和性能评估所有建议的DAPUF设计，并比较他们的对手从XOR PUF。我们从三个FPGA硬件板生成了不同的真实的挑战-响应对CRP集，以使用专用评估指标评估DAPUF和XOR PUF设计的性能。我们表明，没有建议的设计DAPUF是严格优于XOR PUF设计。此外，我们使用神经网络的安全分析揭示了所有DAPUF设计对机器学习攻击的脆弱性。
Classical cryptographic methods that inherently employ secret keys embedded in non-volatile memory have been known to be impractical for limited-resource Internet of Things (IoT) devices. Physical Unclonable Functions (PUFs) have emerged as an applicable solution to provide a keyless means for secure authentication. PUFs utilize inevitable variations of integrated circuits (ICs) components, manifest during the fabrication process, to extract unique responses. Double Arbiter PUFs (DAPUFs) have been recently proposed to overcome security issues in XOR PUF and enhance the tolerance of delay-based PUFs against modeling attacks. This paper provides comprehensive risk analysis and performance evaluation of all proposed DAPUF designs and compares them with their counterparts from XOR PUF. We generated different sets of real challenge–response pairs CRPs from three FPGA hardware boards to evaluate the performance of both DAPUF and XOR PUF designs using special-purpose evaluation metrics. We show that none of the proposed designs of DAPUF is strictly preferred over XOR PUF designs. In addition, our security analysis using neural network reveals the vulnerability of all DAPUF designs against machine learning attacks.