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ATMEGA8515, AES Boolean masking, fixed key, EM acquisition

Getting the ASCAD databases and the trained models

In the new folder, download and decompress the data package with the raw data by using:

$ cd ASCAD/ATMEGA_AES_v1/ATM_AES_v1_fixed_key/
$ wget https://www.data.gouv.fr/s/resources/ascad/20180530-163000/ASCAD_data.zip
$ unzip ASCAD_data.zip

Please be aware that this last step should download around 4.2 GB, and the decompression will generate around 7.3 GB of useful data.

Raw data files hashes

The zip file SHA-256 hash value is:

ASCAD_data.zip a6884faf97133f9397aeb1af247dc71ab7616f3c181190f127ea4c474a0ad72c

We also provide the SHA-256 hash values of the sub-files when this zip archive is decompressed:

ASCAD_databases/ASCAD.h5: f56625977fb6db8075ab620b1f3ef49a2a349ae75511097505855376e9684f91 ASCAD_databases/ASCAD_desync50.h5: 8716a01d4aea2df0650636504803af57dd597623854facfa75beae5a563c0937 ASCAD_databases/ASCAD_desync100.h5: f6b9e967af287e82f0a152320e58f8f0ded35cd74d499b5f7b1505a5ce338b8e ASCAD_databases/ATMega8515_raw_traces.h5: 51e722f6c63a590ce2c4633c9a9534e8e1b22a9cde8e4532e32c11ac089d4625

ASCAD_trained_models/mlp_best_ascad_desync0_node200_layernb6_epochs200_classes256_batchsize100.h5: d97a6e0f742744d0854752fce506b4a0612e0b86d0ec81a1144aada4b6fb35a3 ASCAD_trained_models/mlp_best_ascad_desync50_node200_layernb6_epochs200_classes256_batchsize100.h5 582a590c69df625fd072f837c98e147a83e4e20e04465ff48ca233b02bc75925 ASCAD_trained_models/mlp_best_ascad_desync100_node200_layernb6_epochs200_classes256_batchsize100.h5: 9f4d761197b91b135ba24dd84104752b7e32f192ceed338c26ddba08725663a9 ASCAD_trained_models/cnn_best_ascad_desync0_epochs75_classes256_batchsize200.h5: 11ff0613d71ccd026751cb90c2043aff24f98adb769cb7467e9daf47567645be ASCAD_trained_models/cnn_best_ascad_desync50_epochs75_classes256_batchsize200.h5: be9045672095a094d70d2ee1f5a76277cab6a902c51e4ebf769282f464828a11 ASCAD_trained_models/cnn_best_ascad_desync100_epochs75_classes256_batchsize200.h5: 866d3ea0e357e09ff30fdc9c39b6ef3096262c50cebd42018a119b1190339fcc

The ATMega8515 SCA traces databases

This database contains 60,000 traces from the acquisition campaign compiled in a HDF5 file of 5.6 GB named ATMega8515_raw_traces.h5. The structure of this HDF5 file is described in the article "Study of Deep Learning Techniques for Side-Channel Analysis and Introduction to ASCAD Database".

We emphasize that these traces are synchronized, and that the key is fixed for all the acquisitions.

The ASCAD databases

The databases, which are HDF5 files, basically contain two labeled datasets:

  • A 50,000 traces profiling dataset that is used to train the (deep) Neural Networks models.
  • A 10,000 traces attack dataset that is used to check the performance of the trained models after the profiling phase.

The details of the ASCAD HDF5 structure are given in the article, as well as a thorough discussion about the elements that need to be addressed when applying Deep Learning techniques to SCA.

The ASCAD database is in fact extracted from the ATMega8515_raw_traces.h5 file containing raw traces: in order to avoid useless heavy data processing, only the 700 samples of interest are kept (these samples correspond to the time window of the leaking operation under attack, see the article for details).

The ../ASCAD_generate.py script has been used to generate ASCAD from the ATMega8515_raw_traces.h5 original database. Actually, the repository contains three HDF5 ASCAD databases:

  • ASCAD_data/ASCAD_databases/ASCAD.h5: this corresponds to the original traces extracted without modification.
  • ASCAD_data/ASCAD_databases/ASCAD_desync50.h5: this contains traces desynchronized with a 50 samples maximum window.
  • ASCAD_data/ASCAD_databases/ASCAD_desync100.h5: this contains traces desynchronized with a 100 samples maximum window.

The traces desynchronization has been simulated artificially (and can be tuned) by the python script ../ASCAD_generate.py that generates the database: this allowed us to test the efficiency of Neural Networks against jitter. See the section dedicated to the generation script for details on how to customize the desynchronization parameter.

The trained models

The best trained CNN and MLP models that we have obtained are provided in theASCAD_data/ASCAD_trained_models/ folder. Six models have been selected: best CNNs for desynchronizations 0, 50 and 100, best MLPs for desynchronization values of 0, 50, and 100 time samples.

WARNING: these models are the best ones we have obtained through the methodology described in the article. We certainly do not pretend that they are the optimal models across all the possible ones. The main purpose of sharing ASCAD is precisely to explore and evaluate new models.

We have performed our training sessions on two setups:

  • The first platform is composed of one gamer market Nvidia GeForce GTX 1080 Ti.
  • The second platform is composed of one professional computing market Nvidia Tesla K80.

Both setups were running an Ubuntu 16.04 distro with Keras 2.1.1 and TensorFlow-GPU 1.2.1. When using the GPU acceleration, the computation should not be very CPU and RAM intensive (at most one CPU core work load and 1 to 2 GB of RAM).