Paper 2022/1737

Regularizers to the Rescue: Fighting Overfitting in Deep Learning-based Side-channel Analysis

Azade Rezaeezade, Cyber Security Research Group, Delft University of Technology, The Netherlands
Lejla Batina, Digital Security Group, Radboud University, The Netherlands

Despite considerable achievements of deep learning-based side-channel analysis, overfitting represents a significant obstacle in finding optimized neural network models. This issue is not unique to the side-channel domain. Regularization techniques are popular solutions to overfitting and have long been used in various domains. At the same time, the works in the side-channel domain show sporadic utilization of regularization techniques. What is more, no systematic study investigates these techniques' effectiveness. In this paper, we aim to investigate the regularization effectiveness by applying four powerful and easy-to-use regularization techniques to six combinations of datasets, leakage models, and deep-learning topologies. The investigated techniques are $L_1$, $L_2$, dropout, and early stopping. Our results show that while all these techniques can improve performance in many cases, $L_1$ and $L_2$ are the most effective. Finally, if training time matters, early stopping is the best technique to choose.

Available format(s)
Attacks and cryptanalysis
Publication info
Side-channel Analysis Deep Learning Regularization Overfitting
Contact author(s)
a rezaeezade-1 @ tudelft nl
lejla @ cs ru nl
2022-12-19: approved
2022-12-17: received
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Creative Commons Attribution


      author = {Azade Rezaeezade and Lejla Batina},
      title = {Regularizers to the Rescue: Fighting Overfitting in Deep Learning-based Side-channel Analysis},
      howpublished = {Cryptology ePrint Archive, Paper 2022/1737},
      year = {2022},
      note = {\url{}},
      url = {}
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