Paper 2022/340

To Overfit, Or Not to Overfit: Improving the Performance of Deep Learning-based SCA

Azade Rezaeezade, Guilherme Perin, and Stjepan Picek


Profiling side-channel analysis allows evaluators to estimate the worst-case security of a target. When security evaluations relax the assumptions about the adversary's knowledge, profiling models may easily be sub-optimal due to the inability to extract the most informative points of interest from the side-channel measurements. When used for profiling attacks, deep neural networks can learn strong models without feature selection with the drawback of expensive hyperparameter tuning. Unfortunately, due to very large search spaces, one usually finds very different model behaviors, and a widespread situation is to face overfitting with typically poor generalization capacity. Usually, overfitting or poor generalization would be mitigated by adding more measurements to the profiling phase to reduce estimation errors. This paper provides a detailed analysis of different deep learning model behaviors and shows that adding more profiling traces as a single solution does not necessarily help improve generalization. In fact, we recognize the main problem to be the sub-optimal selection of hyperparameters, which is then difficult to resolve by simply adding more measurements. Instead, we propose to use small hyperparameter tweaks or regularization as techniques to resolve the problem.

Available format(s)
Secret-key cryptography
Publication info
Preprint. MINOR revision.
Side-channel analysisDeep LearningOverfittingGeneralization
Contact author(s)
picek stjepan @ gmail com
2022-03-14: received
Short URL
Creative Commons Attribution


      author = {Azade Rezaeezade and Guilherme Perin and Stjepan Picek},
      title = {To Overfit, Or Not to Overfit: Improving the Performance of Deep Learning-based SCA},
      howpublished = {Cryptology ePrint Archive, Paper 2022/340},
      year = {2022},
      note = {\url{}},
      url = {}
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