Stochastic Selection of Responses for Physically Unclonable Functions

Abstract

Challenges in securing the Internet of Things (IoT) has led to the development of novel technologies such as physically unclonable functions (PUFs). Having applications in both lightweight authentication and key generation protocols for IoT devices, PUFs have received a great deal of research. Despite their promise, delay-based PUFs such as Arbiter PUFs and 4-XOR PUFs are easily modeled with 600 and 50, 000 challenge-response pairs (CRPs), respectively. While it has been shown that delay-based PUFs can be further improved by XORing together an increasing number of PUF instances, it also tends to become area-inefficient. In this paper the authors propose a novel method that combats the effectiveness of machine learning algorithms for modeling PUF behaviors by randomly selecting responses from a pool of PUFs. Six variants to our Random Bit Selection (RBS) PUF are proposed and investigated. The yielded results show that specific variants of RBS PUF are machine learning resistant despite using a 5, 760, 000 CRP dataset for training. Furthermore, the results indicate no significant improvement in the modeling algorithm despite a 100 times increase in the number of CRPs used. Finally, the security of the proposed design is also evaluated through a brute-force analysis to show its resistance to brute-force attacks.