Wavelet Selection and Employment for Side-Channel Disassembly
This work addresses a specific optimization issue for practitioners in side-channel analysis, offering incremental improvements in parameter selection for instruction classification tasks.
This paper tackles the problem of optimizing wavelet selection and analysis parameters for side-channel-based instruction disassembly and classification, demonstrating that these choices significantly impact outcomes, with the gaus1 wavelet at scales 1-21 and grayscale colormap providing the best balance of performance, time, and memory efficiency.
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.