  Side-channel analysis, originally used in cryptanalysis is growing in use
cases, both offensive and defensive. Wavelet analysis is a commonly employed
time-frequency analysis technique used across disciplines, with a variety of
purposes, and has shown increasing prevalence within side-channel literature.
This paper explores wavelet selection and analysis parameters for use in
side-channel analysis, particularly power side-channel-based instruction
disassembly and classification. Experiments are conducted on an ATmega328P
microcontroller and a subset of the AVR instruction set. Classification
performance is evaluated with a time-series convolutional neural network (CNN)
at clock-cycle fidelity. This work demonstrates that wavelet selection and
employment parameters have meaningful impact on analysis outcomes.
Practitioners should make informed decisions and consider optimizing these
factors similarly to machine learning architecture and hyperparameters. We
conclude that the gaus1 wavelet with scales 1-21 and grayscale colormap
provided the best balance of classification performance, time, and memory
efficiency in our application.
