Machine Learning Assisted PUF Calibration for Trustworthy Proof of Sensor Data in IoT

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 Ruhrmair 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 degrees C to 80 degrees 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.