On the Influence of Optimizers in Deep Learning-Based Side-Channel Analysis

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5 Citations (Scopus)


The deep learning-based side-channel analysis represents a powerful and easy to deploy option for profiling side-channel attacks. A detailed tuning phase is often required to reach a good performance where one first needs to select relevant hyperparameters and then tune them. A common selection for the tuning phase are hyperparameters connected with the neural network architecture, while those influencing the training process are less explored. In this work, we concentrate on the optimizer hyperparameter, and we show that this hyperparameter has a significant role in the attack performance. Our results show that common choices of optimizers (Adam and RMSprop) indeed work well, but they easily overfit, which means that we must use short training phases, small profiling models, and explicit regularization. On the other hand, SGD type of optimizers works well on average (slower convergence and less overfit), but only if momentum is used. Finally, our results show that Adagrad represents a strong option to use in scenarios with longer training phases or larger profiling models.

Original languageEnglish
Title of host publicationSelected Areas in Cryptography
Subtitle of host publication27th International Conference, 2020, Revised Selected Papers
EditorsOrr Dunkelman, Michael J. Jacobson, Jr., Colin O’Flynn
Place of PublicationCham
PublisherSpringer Science+Business Media
Number of pages22
ISBN (Electronic)978-3-030-81652-0
ISBN (Print)978-3-030-81651-3
Publication statusPublished - 2021
Event27th International Conference on Selected Areas in Cryptography, SAC 2020 - Virtual, Online at Halifax, Canada
Duration: 21 Oct 202023 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference27th International Conference on Selected Areas in Cryptography, SAC 2020
CityVirtual, Online at Halifax


  • Neural networks
  • Optimizers
  • Profiling attacks
  • Side-channel analysis


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