Wavelet Selection and Employment for Side-Channel Disassembly

TL;DR

This paper investigates how wavelet selection and parameters affect side-channel analysis, demonstrating that optimal choices significantly improve instruction disassembly and classification accuracy on microcontrollers.

Contribution

It systematically evaluates wavelet types and parameters for side-channel disassembly, highlighting the importance of informed selection to enhance analysis effectiveness.

Findings

Gaus1 wavelet with scales 1-21 yields best classification performance.

Wavelet parameters significantly impact disassembly accuracy.

Optimal wavelet choices balance performance, time, and memory efficiency.

Abstract

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.