Embedded cryptography on smartcards can be vulnerable to side-channel attacks, based on the interpretation of the information retrieved during the execution of the algorithm. This information leak is generally measured at the hardware level thanks to a consumption signal or electromagnetic radiation. Many methods, based mainly on statistical tools, exist to exploit these signals and to find secret elements.
However, the information used during this process is partial, because the current methods mainly exploit the signal in the time space. The signals being more and more complex, noisy and out of sync, and also very variable from one component to the other, the application of signal processing methods, in particular a time / frequency analysis, makes it possible to obtain additional information from the frequency space. The use of this information can lead to improved attacks. The state of the art presents several methods around side-channel attacks in frequency domain, but they are currently sparsely exploited.
As a first step, the PhD student will be able to use the existing signals and tools to become familiar with the side-channel attacks. He will then be able to rely on the existing literature around frequency attacks, in particular works of G. Destouet [1-2-3] which explore new techniques for filtering, compression, but also pattern detection for the purpose of optimal resynchronization, or for cutting signals in the context of so-called 'horizontal' attacks.
These researches will be analyzed deeply and the Phd Student will be able to explore new techniques, for example new wavelet bases, and will test his algorithms on suitable signal bases.
Moreover, the 'machine learning' method applied to side-channel attacks is currently studied, and the contribution of frequency data is also a way of improving the use of neural networks. The doctoral student will be able to rely on the different methods already existing in time and expand them thanks to wavelet transforms, in order to improve learning.
These different methods are applicable to signals analysis in voice biometrics. The Phd student will be able, among other things, to study neural networks using frequency data, adapted to audio signals obtained in biometrics, also using wavelets or so-called “cepstral” analysis.

At CEA-Leti Grenoble the student will be in a reference laboratory in the evaluation of high security devices(http://www.leti-cea.fr/cea-tech/leti/Pages/innovation-industrielle/innover-avec-le-Leti/CESTI.aspx).

[1] Gabriel Destouet Ondelettes pour le traitement des signaux compromettants. (Wavelets for side-channel analysis) https://theses.hal.science/tel-03758771
[2] Gabriel Destouet et al. \Wavelet Scattering Transform and Ensemble Methods for Side-Channel Analysis'. In : Constructive Side-Channel Analysis and Secure Design. Sous la dir. de Guido Marco Bertoni et Francesco Regazzoni. T. 12244. Series Title : Lecture Notes in Computer Science. Cham : Springer International Publishing, 2021, p. 71-89. isbn : 978-3-030-68772-4 978-3-030-68773-1. doi : 10 . 1007 / 978 - 3 - 030 -68773-1_4.
[3] Gabriel Destouet et al. Generalized Morse Wavelet Frame Estimation Applied to Side-Channel Analysis. ICFSP 2021: 52-57