Paper 2021/072

Toward Practical Autoencoder-based Side-Channel Analysis Evaluations

Servio Paguada, Lejla Batina, and Igor Armendariz


This paper introduces a practical evaluation procedure based on autoencoders for profiled side-channel analysis evaluations. An autoencoder is a learning model able to pre-process leakage traces improving in this way the guessing entropy. Nevertheless, this learning model's design should aim to code the leakage distribution to avoid relevant information being removed. For this reason, we propose an autoencoder built upon dilated convolutions. When using these learning models, the evaluation produces new assets, e.g., new versions of the dataset and new models based on learning algorithms. Our procedure comprises meaningful metrics and visualization techniques, namely signal-to-noise ratio and weight visualization, to evaluate those assets' effectiveness. After applying our procedure and our new autoencoder architecture to the ASCAD random key database, our results outperform state-of-the-art.

Available format(s)
Publication info
Preprint. MINOR revision.
profiled attacksside-channel analysisdilated convolutionsautoencodersconvolutional neural network
Contact author(s)
servio paguadaisaula @ ru nl
lejla @ cs ru nl
iarmendariz @ ikerlan es
2021-01-22: received
Short URL
Creative Commons Attribution


      author = {Servio Paguada and Lejla Batina and Igor Armendariz},
      title = {Toward Practical Autoencoder-based Side-Channel Analysis Evaluations},
      howpublished = {Cryptology ePrint Archive, Paper 2021/072},
      year = {2021},
      note = {\url{}},
      url = {}
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