The Physical Unclonable Function (PUF) has been proposed for the identification and authentication of devices and cryptographic key generation. A strong PUF provides an extremely large number of device-specific challenge-response pairs (CRP) which can be used for identification. Unfortunately, the CRP mechanism is vulnerable to modeling attack, which uses machine learning (ML) algorithms to predict PUF responses with high accuracy. Many methods have been developed to strengthen strong PUFs with complicated hardware; however, recent studies show that they are still vulnerable by leveraging GPU-accelerated ML algorithms.