> For the complete documentation index, see [llms.txt](https://pace-tl.gitbook.io/ches-challenge-2025/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://pace-tl.gitbook.io/ches-challenge-2025/quickstart-2.md).
# 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](https://drive.google.com/drive/folders/1JGbphwZXQvN_tEhpBIbQ-q-pN9wkqKQ-?usp=sharing).
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](/ches-challenge-2025/quickstart-4.md)).
The private dataset can be downloaded from the following [link](https://drive.google.com/drive/folders/1foaYhR_40_s17vmOdEv0dhKFVB09qksc?usp=drive_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](/ches-challenge-2025/quickstart.md)).
The private dataset can be loaded using the `load_data.py` script available at the provided [link](https://drive.google.com/drive/folders/1foaYhR_40_s17vmOdEv0dhKFVB09qksc?usp=sharing). 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).
