Efficient hybrid side‐channel/machine learning attack on XOR PUFs. Issue 20 (1st October 2019)

• Record Type: Journal Article
• Authors: Yu, Weize; Wen, Yiming
• Is Part Of: Electronics letters. Volume 55:Issue 20(2019)
• Page Start: 1080
• Page End: 1082
• Publication Date: 2019-10-01
• Subjects: learning (artificial intelligence) -- cryptography -- convolutional neural nets -- feature extraction -- digital arithmetic; training‐testing accuracy -- critical feature extraction -- confidential information -- hybrid SC‐machine learning attack -- hybrid side‐channel‐machine learning attack -- regular CNN attack -- convolutional neural network attack -- XOR arbiter PUF -- nonlinear physical unclonable functions; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2019.1363
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : A novel hybrid side‐channel (SC)/machine learning attack is explored in this Letter to leak the confidential information of non‐linear physical unclonable functions (PUFs): XOR arbiter PUFs. In the proposed hybrid attack, SC analyses are utilised to pre‐process the input challenge of XOR arbiter PUFs to add high correlation among all the input challenge bits. Subsequently, a convolutional neural network (CNN) attack is performed on the correlated input challenge bits to extract the critical feature among the neighbour data to significantly improve its training/testing accuracy. As shown in the results, after applying the SC analyses to add correlation for the input challenge of an XOR arbiter PUF, the training/testing accuracy of the hybrid attack can be boosted over 0.98. In contrast, the training/testing accuracy of a regular CNN attack on the XOR arbiter PUF is around 0.64 due to the lack of the corresponding correlation.