Datasets

Downloading the dataset

There will be only one public datasets given to train the neural networks.

The dataset can be downloaded from the following link.

It consists of 500K profiling traces and 100k attack traces.

The hash of the above dataset is as follows:

132ae2e9a8213c983bf3b63449e9572d5d71d3b376a75b236415d4a728b9379f

There will be three private set of attack traces used for evaluation (see Challenge Rule).

The private dataset can be downloaded from the following link. The hash of the private datasets (into one file) is as follow:

86ec5b8fefb6ff9aad88112782ea34b6f3785bf9533c94a9ece51ccf9587ca97

Flow of the public dataset

The datasets are stored in .h5 file. The flow of the public dataset is similar to the ASCAD dataset [1].

The structure of the public dataset is outlined as follows:

Dataset

Note: We will be targeting only the first byte for this challenge (i.e. byte $0$).

Flow of the private dataset

The structure of the private dataset is outlined as follows:

Loading the datasets

The function to load the public dataset can be found in utils.py as the function load_ctf_2025() . This function is used within main_{tf/pytorch}.py and analyze_{tf/pytorch}.py (see Getting Started).

The private dataset can be loaded using the load_data.py script available at the provided link. A README.md file is also included with detailed instructions on how to load the dataset.

Citation

If our dataset contributed to your research, please acknowledge it with the following citation:

@misc{ge_wars2025,
author = {Shivam Bhasin and Harishma Boyapally and Dirmanto Jap and Trevor Yap and Qianmei Wu.},
title = {{GE Wars: The Deep Learning SCA battle}},
howpublished    = {CHES Challenge 2025},
year      = {2025},
note = {https://pace-tl.gitbook.io/ches-challenge-2025}
}

References

1. Benadjila, R., Prouff, E., Strullu, R. et al. Deep learning for side-channel analysis and introduction to ASCAD database. J Cryptogr Eng 10, 163–188 (2020). https://doi.org/10.1007/s13389-019-00220-8