Security Analysis and Modeling Attacks on Duty Cycle Multiplexer PUF The security of strong physical unclonable function (PUF) such as arbiter PUF is becoming a question after machine learning method like logistic regression (LR) is proved to work well on modeling arbiter PUF. Duty cycle multiplexer (DC MUX) PUF is a newly proposed PUF design to enhance the reliability of arbiter PUF. The security of DC MUX PUF is analyzed in this paper, and the relation between error rate and the number of challengeresponse pairs (CRPs) is obtained with the previous theoretical result. Then LR attack on DC MUX PUF is conducted, the results match the theoretical result. The prediction error of DC MUX PUF is both predicted and then proved to be higher than that of arbiter PUF. Besides, the training time of DC MUX PUF is 40000 times higher than arbiter PUF. As a result, DC MUX PUF is proved to be safer than arbiter PUF.

An Obfuscated Challenge Design for APUF to Resist Machine Learning Attacks Physical unclonable function (PUF) has emerged as a lightweight hardware security primitive for resource constrained devices. The arbiter PUF (APUF) is a typical kind of strong PUF. However, conventional APUF is vulnerable to machine learning (ML) attacks. In this paper, we propose an obfuscated challenge design for APUF (OCAPUF), which exchanges the bit positions in the challenge according to our design rules. The subsequent recovery of the obfuscated challenge by a recovery circuit is guaranteed. Then the corresponding response is produced by the APUF with the real challenge. The goal is to obscure the direct relationship between challenges and responses to prevent ML attacks. Most importantly, the unclonability of the APUF is preserved, and there is almost no increase in hardware complexity while still maintaining a high level of security. Experiment results show that 64bit APUF with obfuscated challenge can resist ML attacks with a maximum prediction rate of 60% using the logistic regression (LR) strategy.