Getting started

A dedicated evaluation framework has been developped for the challenge and is available on Github. This section explains the different steps that should be performed in order to run the demo attack provided. More details about the evaluation framework can be found in the dedicated Framework section.

Installing dependencies

The framework runs with python >= 3.8 and requires the following python tools:

  • venv, part of python's standard library, but not included by in some python installations (e.g., on ubuntu, you might have to apt install python3-venv to get it).
  • pip, also, part of most python installations (but on ubuntu, apt install python3-pip is needed).

Additionally, the demonstration attack depends on

  • Yosys (version 0.25 tested, below 0.10 will likely not work)
  • Verilator (use version 5.006, many other version are known not to work)
  • Make

CAUTION: we highly recommand to install Verilator from the git and to run Verilator in-place (as recommended by the official documentation).

We highly recommend to use the challenge in a unix environment (on windows, use WSL).

Cloning repo

First, clone the challenge framework repository:

git clone

Downloading the datasets

See this page for downloading the datasets.

In the following, we use the variable SMAESH_DATASET as the path to the directory where the downloaded dataset is stored (i.e., the path to the directory smaesh-dataset, which is the directory that contains the directories A7_d2 and S6_d2).

As a final step, format the dataset. This operation must be done a single time on each fixed key dataset (it may last a few seconds). This will generate a new manifest per dataset (manifest_split.json) that will be used by the framework's scripts to evaluate the attack.

# Create the venv for running framework's scripts
cd SMAesH-challenge
python3 -m venv venv-scripts
source venv-scripts/bin/activate # Activate it (adapt if not using bash shell)
pip install pip --upgrade 
pip install -r scripts/requirements.txt
# Run the split_dataset command (here for the Artix-7 only)
python3 scripts/ --dataset $SMAESH_DATASET/A7_d2/fk0/manifest.json 
# Leave de venv-scripts virtual environment

Running our example attack: profiling, attack, evaluation

The following steps allow to run the demo attack and to evaluate it.

  1. First, we move to the cloned framework directory
    cd SMAesH-challenge
  2. Then, setup a python virtual environement used for the evaluation, and activate it
    python3 -m venv venv-demo-eval
    source venv-demo-eval/bin/activate
    pip install pip --upgrade 
  3. Install the verime dependency (tool for simulating intermediate values in the masked circuit)
    pip install verime
  4. In the demo submission directory, build the simulation library with Verime
    cd demo_submission
    make -C values-simulations 
  5. Install the python package required to run the attack
    (cd setup && pip install -r requirements.txt)
  6. Run the evaluation in itself
    # Profiling step:
    # - uses the dataset vk0
    # - saves the templates in the current directory
    python3 profile --profile-dataset $SMAESH_DATASET/A7_d2/vk0/manifest.json --attack-case A7_d2 --save-profile .
    # Performs the attack using 16777215 traces 
    # - uses the dataset fk0
    # - loads the profile located into the current directory
    # - performs the attack using 524288 traces
    # - saves the keyguess resulting in the file './keyguess-file'
    python3 attack --attack-dataset $SMAESH_DATASET/A7_d2/fk0/manifest_split.json --attack-case A7_d2 --load-profile . --save-guess ./keyguess-file --n-attack-traces 16777216
    # Evaluates the attack based on the result file produced
    # - loads the keyguess file generated with the attack
    # - use the key value of the dataset fk0 as a reference.
    python3 eval --load-guess ./keyguess-file --attack-case A7_d2 --attack-dataset $SMAESH_DATASET/A7_d2/fk0/manifest_split.json

For the demo attack, the evaluation phase is expected to produce the following result on the standard output when the default configuration are used

log2 ranks [59.79907288]
number of successes 1

which means that the attacks reduces the rank of the correct key to \( 2 ^ {59.79} \).

By default, the profiling phase implemented uses \( 2^{24}\) traces to build the models, which may result in a significant processing time (it takes about 45 minutes on the reference machine). The attack also runs in abouthe same time on that machine. Reducing the number of traces for both steps will reduce their execution time (at the expense of a worse key rank, of course).

Note: you can run multiple steps at once, as in python3 profile attack eval ....

Next steps

It's your turn!

Both phases (profiling and attack) are implemented in the profile() and attack() functions in tweak these functions to implement your revolutionary attack.

If you get the demo submission to run with fewer traces, you can also try to directly submit it!

The other pages of this website provide more detailed information on how to develop a submission. In particular:

  • Framework details how to use the framework of the challenge to develop, evaluate, package and send a new submission.
  • Rules: see how to get points, and what are the constraints on submitted attacks.
  • Target: acquisition setup used for the different targets.
  • Datasets: content of the datasets.

Have a look at our suggestions and at the SMAesH documentation to get ideas for improved attacks.