A masked advanced encryption standard (AES) physical unclonable function (PUF) architecture is proposed for hardware authentication against hybrid side-channel (SCA) and machine-learning attacks (MLAs). The random mismatches of the load capacitance of the masked substitution-boxes in the AES cryptographic circuit induced by the fabrication process are utilised for generating the criticalauthentication data against SCAs. Moreover, a mask data is added to the input challenge data to mask the actual input data of the proposed PUF against MLAs. As demonstrated in the results, the masked AES PUF proposed shows a nearly 51.1% uniformity, 50.7% inter- Hamming distance, and 98.1% reliability. Furthermore, if a hybrid SCA/MLA is performed on the proposed PUF by combining the corresponding side-channel leakage with the deep neural network algorithm, the prediction rate of the output responses of the masked AES PUF is only 55.2% after 100, 000 number of challenge-toresponse pairs are used for training.