An Entropy-Source-Preselection-Based Strong PUF With Strong Resilience to Machine Learning Attacks and High Energy Efficiency

Abstract

This paper presents an entropy-source-preselection-based strong PUF (ESP-PUF). Through the presented entropy-source-preselection scheme, we will convert first the input challenge bits of the ESP-PUF into the entropy selection signals through the front-end selection network, realized based on an XOR tree. Then the entropy selection signals serve as the power-on indicator to randomly select the back-end entropy sources to generate the raw responses. Utilizing this preselection scheme, we can fulfill both ultra-low power and strong resilience to machine learning (ML) attacks. An effective randomness-enhancement block amplifies the randomness of the raw responses thus alleviating their bias. Moreover, we propose an obfuscation-based protection mechanism to further protect the root challenge-response pairs (CRPs) of the entropy sources and enhance the resilience to ML attacks. Fabricated in 65nm CMOS LP technology, the proposed ESP-PUF shows a high energy efficiency of 0.46pJ/bit. Meanwhile, it demonstrates an average bit-error rate (BER) of 5.83% in the worst-case for the temperature range of −20°C to 120°C and a supply voltage variation of ±10%. The proposed CRPs filtering method can suppress the worst-case BER to a value <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt; 6.7\times 10 ^{-6}$ </tex-math></inline-formula> , presenting high stability. The proposed ESP-PUF occupies an active area of 0.0122 mm2. After training for 1M CRPs samples, the prediction accuracy of the adopted ML algorithms is still ~50%, confirming a strong resilience of the proposed ESP-PUF.