Graphene-based physically unclonable functions that are reconfigurable and resilient to machine learning attacks
Graphene-based physically unclonable functions that are reconfigurable and resilient to machine learning attacks
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DOI：
10.1038/s41928-021-00569-x
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发表时间：
2021-05-10
影响因子：
34.3
通讯作者：
Das, Saptarshi
中科院分区：
文献类型：
--
作者：
Dodda, Akhil;Subbulakshmi Radhakrishnan, Shiva;Das, Saptarshi
石墨烯具有一系列特性，使其适用于构建物联网设备。然而，这种设备的部署也可能需要开发合适的基于石墨烯的硬件安全原语。在这里，我们报告了一个物理不可克隆的功能（PUF），利用石墨烯场效应晶体管的载流子传输的障碍。大量石墨烯场效应晶体管的狄拉克电压、狄拉克电导和载流子迁移率值遵循高斯随机分布，这允许器件用作PUF。由此产生的PUF可以抵御基于预测回归模型和生成对抗神经网络的机器学习攻击。由于石墨烯的忆阻特性，PUF也是可重新配置的，而无需任何物理干预和/或额外硬件组件的集成。此外，我们还证明了PUF可以以超低功耗工作，并且可扩展，随时间推移稳定，并且对温度和电源电压的变化可靠。石墨烯场效应晶体管的电荷载流子传输中的无序可用于构建物理不可克隆的功能，这些功能是安全的，可以承受高级计算攻击。
Graphene has a range of properties that makes it suitable for building devices for the Internet of Things. However, the deployment of such devices will also likely require the development of suitable graphene-based hardware security primitives. Here we report a physically unclonable function (PUF) that exploits disorders in the carrier transport of graphene field-effect transistors. The Dirac voltage, Dirac conductance and carrier mobility values of a large population of graphene field-effect transistors follow Gaussian random distributions, which allow the devices to be used as a PUF. The resulting PUF is resilient to machine learning attacks based on predictive regression models and generative adversarial neural networks. The PUF is also reconfigurable without any physical intervention and/or integration of additional hardware components due to the memristive properties of graphene. Furthermore, we show that the PUF can operate with ultralow power and is scalable, stable over time and reliable against variations in temperature and supply voltage.Disorder in the charge carrier transport of graphene-based field-effect transistors can be used to construct physically unclonable functions that are secure and can withstand advanced computational attacks.