Year: 2026
Type: book
Abstract: Deep learning-based side-channel analysis (DLSCA) represents a powerful paradigm for running side-channel attacks.DLSCA, in a state-of-the-art setting, can break multiple targets with a single attack trace, requiring minimal feature engineering.As such, DLSCA is an extremely active research area for both industry and academia.At the same time, due to domain activity, it becomes more difficult to understand the current trends and challenges.In this systematization of knowledge, we provide a critical overview of developments in DLSCA over the past years, enabling us to offer concrete suggestions and taxonomies.Moreover, we examine the reproducibility perspective and show that achieving it in full is difficult and requires a careful experimental setup. Open ScienceWe provide all scripts used in this work at https://doi. org/10.5281/zenodo.20340778.This includes scripts for collecting and filtering citing papers from Google Scholar for the generation of Figure 1, as well as scripts to reproduce the experimental results in Appendix D. Note that our experiments utilized the original public repository of [96] essentially as-is, implementing only minor modifications that can be seen in the provided scripts, while the repository of [110] was used without any changes to any of the scripts.
Authors: Karayalçın, S., Krček, M., Picek, S.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W7204016338