Masked AES PUF: a new PUF against hybrid SCA/MLAs. Issue 10 (1st May 2018)

• Record Type: Journal Article
• Authors: Yu, Weize; Chen, Jia
• Is Part Of: Electronics letters. Volume 54:Issue 10(2018)
• Page Start: 618
• Page End: 620
• Publication Date: 2018-05-01
• Subjects: cryptography -- learning (artificial intelligence) -- neural nets; masked AES PUF architecture -- masked advanced encryption standard physical unclonable function -- hardware authentication -- hybrid side‐channel attacks -- SCA -- machine‐learning attacks -- MLA -- load capacitance -- masked substitution‐boxes -- AES cryptographic circuit -- fabrication process -- critical‐authentication data -- mask data -- input challenge data -- actual input data -- side‐channel leakage -- deep neural network algorithm -- prediction rate -- challenge‐to‐response pairs; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2018.0735
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : 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 critical‐authentication 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‐to‐response pairs are used for training.