A Simple Transformer Pipeline for Full-Key Side-Channel Attacks on Uncropped Datasets

Abstract:Deep learning-based side-channel analysis has historically focused on single-byte targets and manually cropped traces, which risks discarding exploitable leakage. While recent work has proposed specialized architectures and resampling techniques to address this gap, the literature lacks a simple transformer baseline for simultaneous full-key attacks on uncropped traces. We present an open-source transformer implementation for uncropped full-key attacks which uses the standard transformer encoder backbone, adapting only the input and output layers to the side-channel setting. We release our implementation, training recipes, and pretrained weights for uncropped ASCADv1f, ASCADv1r, and CHES-CTF-2018 which achieve performance competitive with previously-reported results, while using less than 10GB of VRAM and requiring at most 3.34 hours of training on a single NVIDIA A6000.

Accepted to the OPTIMIST Workshop '26 at CHES 2026. 6 pages, 1 figure. Code can be found at this https URL

Cryptography and Security (cs.CR); Machine Learning (cs.LG)

arXiv:2608.30105 [cs.CR]

(or arXiv:2608.30105v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2608.30105 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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