论文标题
PUF-on-PUF实施的设计探索和安全评估
Design Exploration and Security Assessment of PUF-on-PUF Implementations
论文作者
论文摘要
我们设计，实施和评估PUF-ON-PUF（POP）体系结构的几种变体的安全性。我们对深神经网络（DNN）进行了广泛的实验，显示了结果在使用6或更多阶段的APUFS时，可以认可其对学习攻击的韧性。使用APUFS具有2个和4个阶段的组成很容易受到DNN攻击的影响。我们反思这种结果，扩展了先前的影响力技术，以评估APUF实例中的阶段偏见。我们的数据表明，组成并不总是保留PUF的安全性，所使用的PUF的大小起着至关重要的作用。我们在65 nm CMO中实施了测试算法，以获得准确的均匀性，独特性和响应稳定性的测量。测量结果表明，当使用第一层的APUFS使用8个阶段时，获得最小位错误率，而较少的APUF阶段会导致在不同芯片上大量的位错误率差异很大。
We design, implement, and assess the security of several variations of the PUF-on-PUF (POP) architecture. We perform extensive experiments with deep neural networks (DNNs), showing results that endorse its resilience to learning attacks when using APUFs with 6, or more, stages in the first layer. Compositions using APUFs with 2, and 4 stages are shown vulnerable to DNN attacks. We reflect on such results, extending previous techniques of influential bits to assess stage bias in APUF instances. Our data shows that compositions not always preserve security properties of PUFs, the size of PUFs used plays a crucial role. We implemented a testchip in 65 nm CMOS to obtain accurate measurements of uniformity, uniqueness, and response stability for our POP implementations. Measurement results show that minimum bit error rate is obtained when using APUFs with 8 stages in the first layer, while fewer APUF stages lead to a large spread of bit error rate across different chips.
