PMTV PUF: Power-Aware and ML-Secure PUF With Tolerance to Temperature and Voltage Fluctuations

Security of data transmission is one of the main challenges in the Internet of Things (IoT) networks. One of the main solutions to ensure security in these networks is physically unclonable functions (PUFs). The performance of the PUFs is highly dependent on voltage and temperature. Moreover, this solution can be successfully attacked by machine learning (ML) approaches. Detecting the possibility of an attack helps in preventing it. In this article, a novel PUF (PMTV PUF) is presented. The proposed circuit uses a pipeline structure. This structure improves the reliability of the PUF under different voltages and temperatures. Pipeline architecture is also used to resist ML attacks. Challenge obfuscation is applied in the pipeline stages. This makes it harder for ML models to learn the behavior. In addition, new techniques for measuring system power consumption are used. These techniques help detect and prevent potential ML attacks. The proposed PMTV PUF circuit is implemented on a Xilinx Zynq 7 FPGA with the proposed power measurement circuit as an extension. Experimental results show the great performance of the responses. The bit error rate (BER) is $3.17 \times 10^{-10}$ (at $150~^{\circ }$ C with a 1.32-V supply voltage), the uniformity is 50.12%, and the uniqueness is 49.87%. In conventional architectures, the average system vulnerability against ML attacks is approximately $\mathbf {9 0 \%}$ . In the proposed architecture, this vulnerability has been reduced to 50.23%. According to the obtained result, the proposed system not only has high reliability but also possesses the ability to detect and prevent potential ML attacks.