Single-Trace Attack Using One-Shot Learning With Siamese Network in Non-Profiled Setting

Nayeon Lee, Seokhie Hong, Heeseok Kim

Research output: Contribution to journalArticlepeer-review


Recently, many studies have shown that using deep learning for side-channel attacks offers several advantages, including simplification of the attack phase and target breaking, even in protected implementations, while presenting outstanding attack performance. Power and electromagnetic analysis, which is known as the most robust attack, can be classified into profiling and non-profiling attacks. In the real world, a non-profiling attack is more ideal than a profiling attack. In particular, studies on non-profiling attacks using deep learning for asymmetric cryptosystems are rare and have shortcomings, such as a long analysis time. In this paper, we propose a novel non-profiling attack method for asymmetric cryptosystems that requires only a single trace and a reasonably short attack time to recover a full private key, overcoming the limitations of previous studies. The proposed method applies one-shot learning with a convolutional Siamese network, which is used for the first time in side-channel attacks. Thus, our proposed method can leak private keys used in a protected public-key cryptosystem with up to 100% accuracy with only one single trace in a non-profiled setting.

Original languageEnglish
Pages (from-to)60778-60789
Number of pages12
JournalIEEE Access
Publication statusPublished - 2022


  • Deep learning
  • ECC
  • Montgomery ladder
  • Siamese network
  • non-profiling attack
  • one-shot learning
  • side channel attack
  • similarity score

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)
  • Electrical and Electronic Engineering


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