A dynamically configurable LFSR-based PUF design against machine learning attacks

Abstract

Physical unclonable function (PUF), a reliable and lightweight physical security primitive for secret key generation and anti-tampering. Strong PUF is an important PUF classification that provides a large “Challenge-Response” pairs (CRP) space for device authentication. However, none of the existing PUF constructions is both machine learning (ML) attack resistant and sufficiently lightweight to fit the low-end internet of things and embedded devices. A lightweight composition PUF design, Shift Register based PUF (SRPUF), is proposed in which the time delay performance is sacrificed to make the PUF structure variable and difficult to derive a stable model. A linear feedback shift register (LFSR) is used to de-synchronized the input challenges and output responses of the SRPUF. The LFSR can be configured dynamically to provide a high entropy source and large enough CRP space. The SRPUF is simulated in Python then implemented on a 28 nm FPGA. The experimental results show that the uniformity and uniqueness of the PUF is 49.8%, 49.9%, which is close to the ideal value, and the hardware overhead is small. Meanwhile, it shows excellent resistance to several popular ML attack methods. This new PUF design idea is suitable for resource-constrained and time delay-insensitive applications.