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Side-channel Attack Project

This is a project in which side-channel attacks are researched and developed.

Features

The software includes a number of features.

Side-channel attacks included in current version:

  • Template attack
  • Pooled template attack
  • Stochastic attack
  • Mutual information analysis

Analysis tools included in current version:

  • Normalized inter class variance analysis
  • Pearson correlation analysis

Other features include:

  • Progressbar
  • Logging of sub result

Getting started

Requirements

This project needs to following to run:

Installing packages

In a terminal, run

python setup.py install

in the root directory (cs-11/ most likely) to install most of the required dependencies.

Running

Python can't find our modules by itself, so we need to tell Python where they are. The command below will append the current directory (which should be the project root directory) to the PYTHONPATH environment variable.

On Linux or if using a Linux shell on Windows:

export PYTHONPATH=$(pwd)

On Windows' cmd:

set PYTHONPATH=%cd%

Then you can run the program:

python sca

Running tests

To verify the correctness of your installation, the test suite can be executed with the following command:

python setup.py test

Documentation

The side-channel attack (sca) program allows the following side-channel attacks to be run:

  • Mutual Information Analysis Attack (mia)
  • Stochastic Attack (sa)
  • (Pooled) Template Attack (ta)
  • Online Correlation Power Analysis (cpa-online)

These simple attacks can be run using the following commands

python sca mia
python sca sa
python sca ta
python sca cpa-online

Flags for the attacks

Flag Info Default Compatibility
--traces-file, -tf The path for the traces to be used Default data set ALL
--keys-file, -kf The path for the keys to be used Default data set ALL
--plain-file, -pf The path for the plain to be used Default data set ALL
--subkey The subkey to find; must be in the range [0-15]. When leaving this option out, the whole key is calculated. MIA: 0, SA: ALL, TA: ALL, CPA-online: ALL ALL
--traces, -t The number of traces to run the attack with. MIA: 1000, SA: 4000, TA: 30, CPA-online: 100 ALL
--num_attack-traces, -a The number of traces which will be attacked MIA: 1000, SA: 30, TA: - MIA, SA, TA
--round, -r The round to attack on. 1 MIA, SA, TA
--num-features, -f Specify the number of features - MIA, SA
--op-substitution Attack the SubBytes operation. 0 MIA, SA, TA
--op-shift-rows Attack the ShiftRows operation. 0 MIA, SA, TA
--op-mix-columns Attack the MixColumns operation. 0 MIA, SA, TA
--op-add-round-key Attack the AddRoundKey operation. 0 MIA, SA, TA
--fs-none Don't use feature selection SOST MIA, SA
--fs-pearson Use pearson for feature selection SOST MIA, SA
--fs-sost Use SOST for feature selection SOST MIA, SA
--fs-nicv Use Nicv for feature selection SOST MIA, SA
--leakage-model, -lm The leakage model used, the default is the hamming weights model, enable the flag to switch to the intermediate values model. True MIA, SA, TA
--debug-mode, -d 'Enables debug mode, a mode in which more detailed information about the execution of the attack is printed and logged False MIA, SA, TA
--points-of-interest, -i The number of points of interest. 5 TA
--pooled, -p Perform pooled template attack instead of normal template attack. False TA
--spacing, -s The spacing between the points of interest. 1 TA

Examples

Running just the simple mia attack on the default traces will not always yield the same subkey guess. This behaviour is caused by the number of traces that are used to run the attack. Increasing the number of traces will give more consistent results

python sca mia --traces 2000

Running an attack with custom numpy datasets can be done as follows

python sca ta -tf data/custom_traces.npy -kf data/custom_keys.npy -pf data/custom_plain.npy

It is possible to attack the AES implementation on different encryption rounds as well as the operation to attack.

This example shows how to attack AES on the shift-rows operation on round three using stochastic attack.

python sca sa --op-shift-rows --round 3

The program provides multiple feature selection tools to use. The program also allows you to run an attack without any feature selection tool. However, not using a feature selection tool will sometimes increase the computation time drastically.

This example shows how to use the pearson feature selection instead of the default SOST feature selection.

python sca mia --fs-pearson

References

If you use this code, please consider citing:

@misc{SCAT,
  author = {Wolfgang Bubberman and Sengim Karayalcin and Matthias Meester and Olaf Braakman and Stjepan Picek},
  title  = {{Side-channel Analysis Toolbox}},
  note   = {{\url{https://github.com/AISyLab/side-channel-analysis-toolbox}}},
  year   = {2020}
}

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This is a project in which side-channel attacks are researched and developed.

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