Remote integrity verification plays a paramount role in resource-constraint devices owing to emerging applications such as Internet-of-Things (IoT), smart homes, e-health, and so on. The concept of Virtual Proof of Reality (VPoR) proposed by Rührmair et al. in 2015 has come up with a Sense-Prove-Validate framework for integrity checking of abundant data generated from billions of connected sensors. It leverages the unreliability factor of Physically Unclonable Functions (PUFs) with respect to ambient parameter variations such as temperature, supply voltages, and so on, and claims to prove the authenticity of the sensor data without using any explicit keys. The state-of-the-art authenticated sensing protocols majorly lack in limited authentications and huge storage overhead. These protocols also assume that the behaviour of the PUF instances varies unpredictably for different levels of ambient factors, which in turn makes them hard to go beyond the theoretical concept. We address these issues in this work 1 and propose a Machine Learning (ML) assisted PUF calibration scheme to predict the Challenge-Response Pair (CRP) behaviour of a PUF instance in a specific environment, given the CRP behaviour in a pivot environment. Here, we present a new class of authenticated sensing protocols where we leverage the beneficence of ML techniques to validate the authenticity and integrity of sensor data over ambient factor variations. The scheme also reduces the storage complexity of the verifier from O ( p * K * l * ( c + r )) to O ( p * l *( c + r )), where p is the number of PUF instances deployed in the framework, l is the number of challenge-response pairs used for authentication, c is the bit lengths of the challenge, r is the response bits of the PUF, and K is the number of levels of ambient factor variations. The scheme alleviates the issue of limited authentication as well, whereby every CRP is used only once for authentication and then deleted from the database. To validate the proposed protocol through actual experiments on FPGA, we propose 5-4 Double Arbiter PUF, which is an extension of Double Arbiter PUFs (DAPUFs) as this design is more suited for FPGA, and implement it on Xilinx Artix-7 FPGAs. We characterise the proposed PUF instance from −20° C to 80° C and use Random Forest --based ML technique to generate a soft model of the PUF instance. This model is further used by the verifier to authenticate the actual PUF circuit. According to the FPGA-based validation, the proposed protocol with DAPUF can be effectively used to authenticate sensor devices across wide variations of temperature values.

中文翻译：

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机器学习辅助 PUF 校准，用于物联网中传感器数据的可信证明

由于物联网 (IoT)、智能家居、电子健康等新兴应用的出现，远程完整性验证在资源受限的设备中发挥着至关重要的作用。Rührmair 等人提出的虚拟现实证明 (VPoR) 的概念。在 2015 年提出了一个感觉-证明-验证用于对数十亿连接传感器产生的大量数据进行完整性检查的框架。它利用物理不可克隆函数 (PUF) 对环境参数变化（如温度、电源电压等）的不可靠性因素，并声称无需使用任何显式密钥即可证明传感器数据的真实性。最先进的认证传感协议主要缺乏有限的认证和巨大的存储开销。这些协议还假设 PUF 实例的行为会因环境因素的不同水平而发生不可预测的变化，这反过来又使它们难以超越理论概念。我们在这项工作中解决了这些问题1并提出机器学习 (ML) 辅助 PUF 校准方案，以预测 PUF 实例在特定环境中的挑战-响应对 (CRP) 行为，给定枢轴环境中的 CRP 行为。在这里，我们提出了一类新的经过身份验证的传感协议，我们利用 ML 技术的优点来验证传感器数据在环境因素变化下的真实性和完整性。该方案还降低了验证者的存储复杂度，从○(p*ķ*l* (C+r）） 到○(p*l*(C+r））， 在哪里p是框架中部署的 PUF 实例的数量，l是用于身份验证的质询-响应对的数量，C是挑战的比特长度，r是 PUF 的响应位，并且ķ是环境因素变化的水平数。该方案缓解了以下问题有限的身份验证同样，每个 CRP 仅用于身份验证一次，然后从数据库中删除。为了通过在 FPGA 上的实际实验来验证所提出的协议，我们提出了 5-4 双仲裁器 PUF，它是双仲裁器 PUF(DAPUF)，因为这种设计更适合 FPGA，并在 Xilinx Artix-7 FPGA 上实现。我们从 −20° 表征提议的 PUF 实例C至 80°C并使用随机森林--基于 ML 技术来生成 PUF 实例的软模型。验证者进一步使用该模型来验证实际的 PUF 电路。根据基于 FPGA 的验证，提出的带有 DAPUF 的协议可以有效地用于在温度值变化很大的情况下对传感器设备进行身份验证。