APUF Faults: Impact, Testing, and Diagnosis
APUF Faults: Impact, Testing, and Diagnosis
复制标题
DOI：
10.23919/date54114.2022.9774575
复制
发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Y. Wei;Tim Fox;Vincent Dumoulin;Wenjing Rao;N. Devroye
中科院分区：
文献类型：
--
作者：
Y. Wei;Tim Fox;Vincent Dumoulin;Wenjing Rao;N. Devroye
仲裁者在物理上无统治的功能（APUFS）是硬件安全性基础，可利用制造随机性来为ICS生成独特的数字指纹。本文理论上和数字检查了APUFS本地的故障 - 设计阶段的掩盖参数故障或过程变化（PV）在制造阶段。我们从统计学上对它们进行建模，并定量解释这些故障如何影响所得的APUF偏见和唯一性。在单个APUF实例上，这些故障在幅度上显示为一些离群增量元素，因此在解决APUF故障时，我们将重点放在这种异常的三角洲元素上。为了检测这种不良的APUF实例并诊断异常的三角洲元素，我们提出了一种测试方法，该方法可以分配一组随机的挑战，以便可以针对特定的三角洲元素，从而在这些集合的响应中形成可感知的偏见。这种低成本方法在检测和诊断异常的三角洲元素的不良APUF方面非常有效。
Arbiter Physically Unclonable Functions (APUFs) are hardware security primitives that exploit manufacturing random-ness to generate unique digital fingerprints for ICs. This paper theoretically and numerically examines the impact of faults native to APUFs - mask parameter faults from the design phase, or process variation (PV) during the manufacturing phase. We model them statistically, and explain quantitatively how these faults affect the resulting APUF bias and uniqueness. On a single APUF instance, these faults manifest as some outlier delta elements in magnitude, thus we focus on such abnormal delta elements when addressing APUF faults. To detect such bad APUF instances and diagnose the abnormal delta elements, we propose a testing methodology which partitions a random set of challenges so that a specific delta element can be targeted, forming a perceivable bias in the responses over these sets. This low-cost approach is highly effective in detecting and diagnosing bad APUFs with abnormal delta element(s).