Abstract
Template attacks and machine learning are two popular approaches to profiled side-channel analysis. In this paper, we aim to contribute to the understanding of their respective strengths and weaknesses, with a particular focus on their curse of dimensionality. For this purpose, we take advantage of a well-controlled simulated experimental setting in order to put forward two important intuitions. First and from a theoretical point of view, the data complexity of template attacks is not sensitive to the dimension increase in side-channel traces given that their profiling is perfect. Second and from a practical point of view, concrete attacks are always affected by (estimation and assumption) errors during profiling. As these errors increase, machine learning gains interest compared to template attacks, especially when based on random forests.
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Notes
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There are indeed variants of SVM and RF that aim to remedy to this issue. Yet, the “probability-like” scores they output are not directly exploitable in the estimation of information theoretic metrics either. For example, we could exhibit examples where probability-like scores of one do not correspond to a success rate of one.
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Acknowledgements
F.-X. Standaert is a research associate of the Belgian Fund for Scientific Research (FNRS-F.R.S.). This work has been funded in parts by the European Commission through the ERC project 280141 (CRASH).
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Lerman, L., Poussier, R., Bontempi, G., Markowitch, O., Standaert, FX. (2015). Template Attacks vs. Machine Learning Revisited (and the Curse of Dimensionality in Side-Channel Analysis). In: Mangard, S., Poschmann, A. (eds) Constructive Side-Channel Analysis and Secure Design. COSADE 2015. Lecture Notes in Computer Science(), vol 9064. Springer, Cham. https://doi.org/10.1007/978-3-319-21476-4_2
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