Modeling Attack Tests and Security Enhancement of the Sub-Threshold Voltage Divider Array PUF

Physical unclonable function (PUF) is widely used as the root of trust in the IoT systems. The sub-threshold voltage divider array PUF was reported as an anti-modeling-attack PUF. It utilizes the nonlinear I-V relationship in the sub-threshold region of MOS transistors to improve the security. However, the security of this PUF has not been soundly analyzed. This work presents the simulation results of modeling attack tests which reveal the vulnerability of this PUF. In the attack, the sub-threshold voltage divider array PUF is modeled by a dedicated artificial neural network (ANN). The nonlinearity of the PUF is simplified based on its working principle. The simulation results show that the prediction accuracy achieves 97% when the number of training CRPs is 350 for the single-stage PUF, and it achieves 90% when the number of training CRPs is 300 for the multi-stage PUF. Furthermore, this work improves the original structure of the sub-threshold voltage divider array PUF, to enhance its anti-modeling-attack capability. The simulation results of modeling attack tests show that the prediction accuracy of the improved multi-stage PUF is reduced to about 50%, which implies that the improved PUF has a strong anti-modeling-attack capability.