A Machine Learning Attack Resilient True Random Number Generator Based on Stochastic Programming of Atomically Thin Transistors,ACS Nano

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A Machine Learning Attack Resilient True Random Number Generator Based on Stochastic Programming of Atomically Thin Transistors
ACS Nano ( IF 15.8 ) Pub Date : 2021-10-19 , DOI: 10.1021/acsnano.1c05984
Akshay Wali ₁ , Harikrishnan Ravichandran ₂ , Saptarshi Das _{1,

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Affiliation

A true random number generator (TRNG) is a critical hardware component that has become increasingly important in the era of Internet of Things (IoT) and mobile computing for ensuring secure communication and authentication schemes. While recent years have seen an upsurge in TRNGs based on nanoscale materials and devices, their resilience against machine learning (ML) attacks remains unexamined. In this article, we demonstrate a ML attack resilient, low-power, and low-cost TRNG by exploiting stochastic programmability of floating gate (FG) field effect transistors (FETs) with atomically thin channel materials. The origin of stochasticity is attributed to the probabilistic nature of charge trapping and detrapping phenomena in the FG. Our TRNG also satisfies other requirements, which include high entropy, uniformity, uniqueness, and unclonability. Furthermore, the generated bit-streams pass NIST randomness tests without any postprocessing. Our findings are important in the context of hardware security for resource constrained IoT edge devices, which are becoming increasingly vulnerable to ML attacks.

中文翻译：

基于原子级薄晶体管随机规划的机器学习攻击弹性真随机数发生器

真随机数发生器 (TRNG) 是关键的硬件组件，在物联网 (IoT) 和移动计算时代，它对于确保安全的通信和身份验证方案变得越来越重要。尽管近年来基于纳米级材料和设备的 TRNG 激增，但它们对机器学习 (ML) 攻击的弹性仍未得到检验。在本文中，我们通过利用具有原子级薄沟道材料的浮栅 (FG) 场效应晶体管 (FET) 的随机可编程性，展示了 ML 攻击弹性、低功耗和低成本的 TRNG。随机性的起源归因于 FG 中电荷俘获和释放现象的概率性质。我们的 TRNG 还满足其他要求，包括高熵、均匀性、唯一性和不可克隆性。此外，生成的比特流无需任何后处理即可通过 NIST 随机性测试。我们的发现在资源受限的物联网边缘设备的硬件安全性方面很重要，这些设备越来越容易受到 ML 攻击。

更新日期：2021-11-23

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