The current repository is associated with the article "Methodology for efficient CNN architectures in Profiling Attacks" available on IACR Transactions on Cryptographic Hardware and Embedded Systems (TCHES) and the eprints

Each dataset is composed of the following scripts and repositories:

• cnn_architecture.py: provides the script in order to train the model introduced in the article,
• exploit_pred.py: computes the evolution of the right key and saves the resulted picture,
• (Optionnal) clr.py: computes the One-Cycle Policy (see "Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates " and "A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay,
• "training_history": contains information related to the loss function and the accuracy,
• "model_predictions": contains information related to the model predictions,
• "fig": contains the figure related to the rank evolution,
• "..._trained_models": containts the model used in the article.

The trace sets were obtained from publicly databases:

• DPA-contest v4: http://www.dpacontest.org/v4/42_traces.php
• AES_HD dataset: https://github.com/AESHD/AES_HD_Dataset
• AES_RD dataset: https://github.com/ikizhvatov/randomdelays-traces
• ASCAD: https://github.com/ANSSI-FR/ASCAD

The zip file SHA-256 hash value is:

AES_HD/AES_HD_dataset.zip: 00a3d02f01bae8c4fcefda33e3d1adb57bed0509ded3cdcf586e213b3d87e41b

AES_RD/AES_RD_dataset/AES_RD_attack.zip: 379c0e29e7f2b7e24ca2ece40b83200b083d48afabd6eabbb01f8ed38a42ebcf AES_RD/AES_RD_dataset/AES_RD_profiling.zip: 93a77b83df7e54656fce798c184e4fb4e3cdc5a740758c0432bdb8c7bd58154d

ASCAD/N=0/ASCAD_dataset.zip: 5f5924e2d0beca5b57fbc48ace137dbb2fe12dd03976aa38f4a699ab21e966b0 ASCAD/N=50/ASCAD_dataset.zip: 9bf704727390a73cf67d3952bc2cacef532b0b62e55f85d615edaa6cd8521f51 ASCAD/N=100/ASCAD_dataset.zip: 2d803db27e58fec3d805cd3cf039b303cad1e0c9ea7a8102a07020bd07113cd9

DPA-contest v4/DPAv4_dataset.zip: c42e0626793848ad38634f1765354fbecd9df3fa606ceb593a94febe6ebeda1f

If you use our code, models or wish to refer to our results, please use the following BibTex entry:

@article{Zaid_Bossuet_Habrard_Venelli_2019, 
title={Methodology for Efficient CNN Architectures in Profiling Attacks},
volume={2020},
url={https://tches.iacr.org/index.php/TCHES/article/view/8391}, 
DOI={10.13154/tches.v2020.i1.1-36}, 
number={1}, 
journal={IACR Transactions on Cryptographic Hardware and Embedded Systems}, 
author={Zaid, Gabriel and Bossuet, Lilian and Habrard, Amaury and Venelli, Alexandre}, 
year={2019}, 
month={Nov.}, 
pages={1-36} 
}