Welcome to the resource topic for 2020/310
Title:
Wavelet Scattering Transform and Ensemble Methods for Side-Channel Analysis
Authors: Gabriel Destouet, Cécile Dumas, Anne Frassati, Valérie Perrier
Abstract:Recent works in side-channel analysis have been fully relying on training classification models to recover sensitive information from traces. However, the knowledge of an attacker or an evaluator is not taken into account and poorly capturedby solely training a classifier on signals. This paper proposes to inject prior information in preprocessing and classification in order to increase the performance of side-channel attacks (SCA). First wepropose to use the Wavelet Scattering Transform, recently proposed by Mallat, for mapping traces into a time-frequency space which is stable under small translation and diffeomorphism. That way, we address the issues of desynchronization and deformation generally present in signals for SCA. The second part of our paper extends the canonical attacks over byteand Hamming weight by introducing a more general attack. Classifiers are trained on different labelings of the sensitive variable and combined by minimizing a cross-entropy criterion so as to find the best labeling strategy. With these two key ideas, we successfully increase the performance of Template Attacks on artificially desynchronized traces and signals from a jitter-protected implementation.
ePrint: https://eprint.iacr.org/2020/310
See all topics related to this paper.
Feel free to post resources that are related to this paper below.
Example resources include: implementations, explanation materials, talks, slides, links to previous discussions on other websites.
For more information, see the rules for Resource Topics .