Paper 2019/1242

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

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


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.

Note: This paper will be submitted a specific conference.

Available format(s)
Secret-key cryptography
Publication info
Preprint. MINOR revision.
Non-profiled side channel attackDeep learningMulti-layer perceptronConvolutional neural networkBinaizred neural network
Contact author(s)
yooseung won @ ntu edu sg
djap @ ntu edu sg
sbhasin @ ntu edu sg
christa @ kookmin ac kr
jonyeon park @ samsung com
2020-04-15: revised
2019-10-23: received
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Creative Commons Attribution


      author = {Jong-Yoen Park and Dong-Guk Han and Dirmanto Jap and Shivam Bhasin and Yoo-Seung Won},
      title = {Non-Profiled Side Channel Attack based on Deep Learning using Picture Trace},
      howpublished = {Cryptology ePrint Archive, Paper 2019/1242},
      year = {2019},
      note = {\url{}},
      url = {}
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