Project overview
This repository contains the code for the paper Profiling Side-Channel Attacks on Dilithium: A Small Bit-Fiddling Leak Breaks It All.
The code is structured in the following way:
secret_key_retrieval contains the source code for the secret key retrieval as described in section 5.
Most of the attack logic is implemented in secret_key_retrieval/attack/side_channel_attack.cpp,
secret_key_retrieval/attack/ilp_solver.cpp and ``secret_key_retrieval/attack/integer_lwe.cpp`.
The project uses the NTL library and SCIP solver to solve the ILP.
The machine learning setup can be found in proof_of_concept/machine_learning and
the chipwhisperer firmware in proof_of_concept/chipwhisperer/victims/unpack_xof/
Usage instructions
- First, run the docker like so:
./run_ntl_docker.sh
This will create a docker containing NTL and SCIP and will give you a shell in that docker.
2. In the docker container, navigate into /current_dir. Here, your current directory will be mounted
cd current_dir/secret_key_retrieval/src
- Build the project (with NIST security level 2)
./build.sh
- For the theoretical evaluations, run:
./build/main <false-positive rate> <true positive rate> <threshold>
where threshold is the maximum value for |z_{i,j}| for which the possibility that y_{i,j} = 0 is not dismiseed, see section 6 of the paper. This will store the results in "ilp_evaluation.csv" 5. To generate the data for the proof of concept evaluation on the chipwhisperer, do:
mkdir poc_data
./build/generate_data poc_data
This will store the public key, private key, the y-coefficients and the generated signatures and the respective inputs to the polyz_unpack function in
poc_data
Now, change to the project chipwhisperer_evaluation.
Run the ./setup_chipwhisperer.sh script. This will copy the unpack
target in the right place in the chipwhisperer repo.
To collect training data, first generate the inputs for the unpack function:
cd machine_learning/generate_data
make
./main_xof | tee xof_data.txt # Let it run for a while
cd ..
Then run:
python3 cw_collect_training_data.py
while the chipwhisperer with the STMF32F4 target is connected to your laptop. This will collect the training data: Then run:
python3 train_classifier.py
To deduce the hyperparameters of the 4 machine-learning models that predict the zero-coefficients. The scripts also trains the models and evaluates the results on a test set. Then, run the polyz_unpack function with the inputs generated in the earlier step and predict the coefficients like so:
python3 unpack_send_xof.py
This will list the cofficients predicted to be zero in the file predicted_zero.txt.
As the last step, run the ./build/execute_attack binary to retrieve the secret key (and test if the the attack worked):
./build/execute_attack <path to poc_data> <path to predicted_zero.txt>
This will read the public key, the signatures and the coefficients that are predicted to be zero and launch attack as described in section 5.