  Strong physical unclonable function (PUF) is a promising solution for device
authentication in resourceconstrained applications but vulnerable to machine
learning attacks. In order to resist such attack, many defenses have been
proposed in recent years. However, these defenses incur high hardware overhead,
degenerate reliability and are inefficient against advanced machine learning
attacks such as approximation attacks. In order to address these issues, we
propose a Random Set-based Obfuscation (RSO) for Strong PUFs to resist machine
learning attacks. The basic idea is that several stable responses are derived
from the PUF itself and pre-stored as the set for obfuscation in the testing
phase, and then a true random number generator is used to select any two keys
to obfuscate challenges and responses with XOR operations. When the number of
challenge-response pairs (CRPs) collected by the attacker exceeds the given
threshold, the set will be updated immediately. In this way, machine learning
attacks can be prevented with extremely low hardware overhead. Experimental
results show that for a 64x64 Arbiter PUF, when the size of set is 32 and even
if 1 million CRPs are collected by attackers, the prediction accuracies of
Logistic regression, support vector machines, artificial neural network,
convolutional neural network and covariance matrix adaptive evolutionary
strategy are about 50% which is equivalent to the random guessing.
