Paper 2019/570

Bias-variance Decomposition in Machine Learning-based Side-channel Analysis

Daan van der Valk and Stjepan Picek


Machine learning techniques represent a powerful option in profiling side-channel analysis. Still, there are many settings where their performance is far from expected. In such occasions, it is very important to understand the difficulty of the problem and the behavior of the machine learning algorithm. To that end, one needs to investigate not only the performance of machine learning but also to provide insights into its explainability. One tool enabling us to do this is the bias-variance decomposition where we are able to decompose the predictive error into bias, variance, and noise. With this technique, we can analyze various scenarios and recognize what are the sources of problem difficulty and how additional measurements/features or more complex machine learning models can alleviate the problem. While such results are promising, there are still drawbacks since often it is not easy to connect the performance of side-channel attack and performance of a machine learning classifier as given by the bias-variance decomposition. In this paper, we propose a new tool for analyzing the performance of machine learning-based side-channel attacks -- the Guessing Entropy Bias-Variance Decomposition. With it, we are able to better understand the performance of various machine learning techniques and understand how a change in a setting influences the performance of an attack. To validate our claims, we give extensive experimental results for a number of different settings.

Available format(s)
Publication info
Preprint. Minor revision.
Side-channel analysisMachine learningDeep learningBias-variance decompositionLoss function
Contact author(s)
picek stjepan @ gmail com
2019-06-15: revised
2019-05-27: received
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Creative Commons Attribution


      author = {Daan van der Valk and Stjepan Picek},
      title = {Bias-variance Decomposition in Machine Learning-based Side-channel Analysis},
      howpublished = {Cryptology ePrint Archive, Paper 2019/570},
      year = {2019},
      note = {\url{}},
      url = {}
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