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DLPFA: Deep Learning based Persistent Fault Analysis against Block Ciphers
Authors: Yukun Cheng, Changhai Ou, Fan Zhang, Shihui ZhengAbstract:
Deep learning techniques have been widely used in side-channel analysis (SCA) in recent years and shown better performance compared with traditional methods. However, there has been little research dealing with deep learning techniques in fault analysis to date. This article undertakes the first study to introduce deep learning into fault analysis. We investigate the application of multi-layer perceptron (MLP) and convolutional neural network (CNN) in persistent fault analysis (PFA) and propose deep learning-based persistent fault analysis (DLPFA). DLPFA is first applied to advanced encryption standard (AES) to verify its availability. Then, to push the study further, we extend DLPFA to PRESENT, which is a lightweight substitution–permutation network (SPN)-based block cipher. The experimental results show that DLPFA can handle random faults and provides outstanding performance with a suitable selection of hyper-parameters.
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