Biblio

The damage caused by counterfeits of semiconductors has become a serious problem. Recently, a physical unclonable function (PUF) has attracted attention as a technique to prevent counterfeiting. The present study investigates an arbiter PUF, which is a typical PUF. The vulnerability of a PUF against machine-learning attacks has been revealed. It has also been indicated that the output of a PUF is inverted from its normal output owing to the difference in environmental variations, such as the changes in power supply voltage and temperature. The resistance of a PUF against machine-learning attacks due to the difference in environmental variation has seldom been evaluated. The present study evaluated the resistance of an arbiter PUF against machine-learning attacks due to the difference in environmental variation. By performing an evaluation experiment using a simulation, the present study revealed that the resistance of an arbiter PUF against machine-learning attacks due to environmental variation was slightly improved. However, the present study also successfully predicted more than 95% of the outputs by increasing the number of learning cycles. Therefore, an arbiter PUF was revealed to be vulnerable to machine-learning attacks even after environmental variation.