Paper 2021/242

GAP: Born to Break Hiding

Ju-Hwan Kim, Ji-Eun Woo, Soo-Jin Kim, So-Yeon Park, and Dong-Guk Han


Recently, Machine Learning (ML) is widely investigated in the side-channel analysis (SCA) community. As an artificial neural network can extract the feature without preprocessing, ML-based SCA methods relatively less rely on the attacker's ability. Consequently, they outperform traditional methods. Hiding is a countermeasure against SCA that randomizes the moments of manipulating sensitive data. Since hiding could disturb the neural network's learning, an attacker should design a proper architecture against hiding. In this paper, we propose inherently robust architecture against every kind of desynchronization. We demonstrated the proposed method with plenty of datasets, including open datasets. As a result, our method outperforms state-of-the-art on every dataset.

Available format(s)
Secret-key cryptography
Publication info
Preprint. Minor revision.
Side-Channel AnalysisDeep LearningGlobal Average PoolingDesynchronizationHiding
Contact author(s)
zzzz2605 @ kookmin ac kr
2021-03-02: received
Short URL
Creative Commons Attribution


      author = {Ju-Hwan Kim and Ji-Eun Woo and Soo-Jin Kim and So-Yeon Park and Dong-Guk Han},
      title = {GAP: Born to Break Hiding},
      howpublished = {Cryptology ePrint Archive, Paper 2021/242},
      year = {2021},
      note = {\url{}},
      url = {}
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