Cryptology ePrint Archive: Report 2019/1242

Non-Profiled Side Channel Attack based on Deep Learning using Picture Trace

Jong-Yoen Park and Dong-Guk Han and Dirmanto Jap and Shivam Bhasin and Yoo-Seung Won

Abstract: In this paper, we suggest a new format for converting side channel traces to fully utilize the deep learning schemes. Due to the fact that many deep learning schemes have been advanced based on MNIST style datasets, we convert from raw-trace based on float or byte data to picture-formatted trace based on position. This is induced that the best performance can be acquired from deep learning schemes. Although the overfitting cannot be avoided in our suggestion, the accuracy for validation outperforms to previous results of side channel analysis based on deep learning. Additionally, we provide a novel criteria for attack success or fail based on statistical confidence level rather than rule of thumb. Even though the data storage is slightly increased, our suggestion can completely be recovered the correct key compared to previous results. Moreover, our suggestion scheme has a lot of potential to improve side channel attack.

Category / Keywords: secret-key cryptography / Non-profiled side channel attack, Deep learning, Multi-layer perceptron, Convolutional neural network, Binaizred neural network

Date: received 22 Oct 2019, last revised 15 Apr 2020

Contact author: yooseung won at ntu edu sg, djap at ntu edu sg, sbhasin at ntu edu sg, christa at kookmin ac kr, jonyeon park at samsung com

Available format(s): PDF | BibTeX Citation

Note: This paper will be submitted a specific conference.

Version: 20200415:131152 (All versions of this report)

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