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Strong physical unclonable function (PUF) is a promising solution for deviceauthentication in resourceconstrained applications but vulnerable to machinelearning attacks. In order to resist such attack, many defenses have beenproposed in recent years. However, these defenses incur high hardware overhead,degenerate reliability and are inefficient against advanced machine learningattacks such as approximation attacks. In order to address these issues, wepropose a Random Set-based Obfuscation (RSO) for Strong PUFs to resist machinelearning attacks. The basic idea is that several stable responses are derivedfrom the PUF itself and pre-stored as the set for obfuscation in the testingphase, and then a true random number generator is used to select any two keysto obfuscate challenges and responses with XOR operations. When the number ofchallenge-response pairs (CRPs) collected by the attacker exceeds the giventhreshold, the set will be updated immediately. In this way, machine learningattacks can be prevented with extremely low hardware overhead. Experimentalresults show that for a 64x64 Arbiter PUF, when the size of set is 32 and evenif 1 million CRPs are collected by attackers, the prediction accuracies ofLogistic regression, support vector machines, artificial neural network,convolutional neural network and covariance matrix adaptive evolutionarystrategy are about 50% which is equivalent to the random guessing.

Set-based Obfuscation for Strong PUFs against Machine Learning Attacks