Paper 2021/071

Reinforcement Learning for Hyperparameter Tuning in Deep Learning-based Side-channel Analysis

Jorai Rijsdijk, Lichao Wu, Guilherme Perin, and Stjepan Picek


Deep learning represents a powerful set of techniques for profiling side-channel analysis. The results in the last few years show that neural network architectures like multilayer perceptron and convolutional neural networks give strong attack performance where it is possible to break targets protected with various countermeasures. Considering that deep learning techniques commonly have a plethora of hyperparameters to tune, it is clear that such top attack results can come with a high price in preparing the attack. This is especially problematic as the side-channel community commonly uses random search or grid search techniques to look for the best hyperparameters. In this paper, we propose to use reinforcement learning to tune the convolutional neural network hyperparameters. In our framework, we investigate the Q-Learning paradigm and develop two reward functions that use side-channel metrics. We mount an investigation on three commonly used datasets and two leakage models where the results show that reinforcement learning can find convolutional neural networks exhibiting top performance while having small numbers of trainable parameters. We note that our approach is automated and can be easily adapted to different datasets. Several of our newly developed architectures outperform the current state-of-the-art results. Finally, we make our source code publicly available.

Available format(s)
Publication info
A minor revision of an IACR publication in TCHES 2021
Side-channel AnalysisDeep learningReinforcement learningRewardQ-policyHyperparameter tuningConvolutional Neural Networks
Contact author(s)
picek stjepan @ gmail com
jorai @ jrijsdijk com
lichao wu9 @ gmail com
guilhermeperin7 @ gmail com
2021-11-11: last of 2 revisions
2021-01-22: received
See all versions
Short URL
Creative Commons Attribution


      author = {Jorai Rijsdijk and Lichao Wu and Guilherme Perin and Stjepan Picek},
      title = {Reinforcement Learning for Hyperparameter Tuning in Deep Learning-based Side-channel Analysis},
      howpublished = {Cryptology ePrint Archive, Paper 2021/071},
      year = {2021},
      doi = {10.46586/tches.v2021.i3.677-707},
      note = {\url{}},
      url = {}
Note: In order to protect the privacy of readers, does not use cookies or embedded third party content.