Efficient hybrid side‐channel/machine learning attack on XOR PUFs
Weize Yu, Yiming Wen · Electronics Letters · 2019
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.