Leakage Models

This module contains pre-defined leakage models that can be used during side-channel analysis experiments. For example, these leakage models are useful when generating the predicted leakage for Pearson’s correlation coefficient.

leakage_model_hamming_weight(num_traces, plaintexts, subkey_guess, target_byte)

Generates hypothetical leakage based on the hamming weight of the AES sbox.

Parameters:

num_traces (int) – The number of traces collected when measuring the observed leakage
plaintexts (list | np.ndarray) – The array of plaintexts used to collect the observed leakage
subkey_guess (any) – the subkey guess
target_byte (int) – the target byte of the key

Returns:

numpy array of the hypothetical leakage

Return type:

np.ndarray

Authors:

Samuel Karkache (swkarkche@wpi.edu)

leakage_model_hamming_distance(num_traces, plaintexts, subkey_guess, target_byte)

Generates hypothetical leakage using the damming distance leakage model. In this implementation the reference state is the output of the sbox at index 0.