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README.md

SCNet: Neural networks for side channel attacks [pdf]

Authors:

Guanlin Li (leegl@sdas.org)

Chang Liu (chang015@e.ntu.edu.sg)

Han Yu (han.yu@ntu.edu.sg)

Yanhong Fan (fanyh@sdas.org)

Libang Zhang (zlb17@mails.tsinghua.edu.cn)

Zongyue Wang (zongyue.wang@opsefy.com)

Meiqin Wang (mqwang@sdu.edu.cn)

Datasets:

ASCAD dataset:

https://github.com/ANSSI-FR/ASCAD

DPA_v4.2 dataset:

DPA Tools

DPA_v4.2 full version dataset

Firstly, download and install the DPA Tools. There are two versions for Win and Unix(Linux and MacOS) respectively. Then, download the index file and traces and unzip the traces into 16 folders respectively.

path  
│
└───k00
│      DPACV42_000000.trc.bz2
│      ...
│      DPACV42_004999.trc.bz2
│    
│   
└───k01
│      DPACV42_005000.trc.bz2
│      ...
│      DPACV42_005000.trc.bz2
│
└───...    
│    
│
│
│
└───k15
        DPACV42_075000.trc.bz2
        ...
        DPACV42_079999.trc.bz2
    

Change the path in read_trace.py depending on your OS.

python read_trace.py

It will spend about 5 days processing all the traces. After finishing processing the traces, change the path in data_gen.py. You can decide which key byte you want to restore and change the target_points corresponding to it. The default profile corresponds to the 11-th byte with 500 target_points.

pip install h5py numpy
python data_gen.py

You can use our DPA dataset directly if you don't want to change any thing about the attack. It is only 38MB and is handy for ML researchers to use.

Models:

We provide trained models on four datasets, three of them from ASCAD and one from DPA_v4.2, respectively. Our experiment environment is tensorflow-gpu==1.8.0, keras==2.2.2, CUDA 9.1.85, cuDNN 7.0. We strongly recommend using Anaconda3 to install them.

With pip to install (you need to install CUDA and cuDNN by yourself):
pip install tensorflow-gpu keras matplotlib
With conda to install:
conda install tensorflow-gpu==1.8.0
conda install keras

If you want to train your own models, just make sure all paths are correct and

on three datasets of ASCAD

python ASCAD_train_models_v1.py

or

python ASCAD_train_models_v2.py

on DPA_v4.2

python DPA_train_models_v1.py

or

python DPA_train_models_v2.py

After finishing training, you can visualize the result on testset with a script

on three datasets of ASCAD

python ASCAD_test_models_v1.py

or

python ASCAD_test_models_v2.py

on DPA_v4.2

python DPA_test_models_v1.py

or

python DPA_test_models_v2.py

SCNet_seq structure:

an image

SCNet structure:

an image

Results:

Result on ASCAD.h5:

an image

Result on ASCAD_desync50.h5:

an image

Result on ASCAD_desync100.h5:

an image

Result on DPA.h5:

an image

Model Name ASCAD Desync0 Requires ASCAD Desync50 Requires ASCAD Desync100 Requires DPA_v4.2 Desync0 Requires
ASCAD CNN 150 4570 None None
SCNet_seq 80 1970 2760 1690
SCNet 160 530 3700 1200

Notice:

We test our code on both Windows 7 and Ubuntu 16.04.

SCNet_v1 → SCNet_seq

SCNet_v2 → SCNet

Cite:

If you use this code, please cite our paper:

@ARTICLE{2020arXiv200800476L,
       author = {{Li}, Guanlin and {Liu}, Chang and {Yu}, Han and {Fan}, Yanhong and
         {Zhang}, Libang and {Wang}, Zongyue and {Wang}, Meiqin},
        title = "{SCNet: A Neural Network for Automated Side-Channel Attack}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Cryptography and Security, Computer Science - Machine Learning},
         year = 2020,
        month = aug,
          eid = {arXiv:2008.00476},
        pages = {arXiv:2008.00476},
archivePrefix = {arXiv},
       eprint = {2008.00476},
 primaryClass = {cs.CR},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020arXiv200800476L},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

This code is for protyping research ideas; thus, please use this code only for non-commercial purpose only.

Credits:

The part of the base codes are borrowed from ANSSI-FR/ASCAD. Thanks for their standardizing implementation of training and test scripts and datasets.

@misc{cryptoeprint:2018:053,
    author = {Emmanuel Prouff and Remi Strullu and Ryad Benadjila and Eleonora Cagli and Cecile Dumas},
    title = {Study of Deep Learning Techniques for Side-Channel  Analysis and Introduction to ASCAD Database},
    howpublished = {Cryptology ePrint Archive, Report 2018/053},
    year = {2018},
    note = {\url{https://eprint.iacr.org/2018/053}},
}

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