07-session-dpa-example.py

# Based on what's introducted in the previous pages, this tutorial will now show
# how to setup a classical Differential Power Attack (DPA) using Lascar.
# (see https://www.paulkocher.com/doc/DifferentialPowerAnalysis.pdf)
#
# For that, a `DpaEngine` is instanciated and registered it to a `Session`.
#
# (The folder lascar/examples/base contains examples for different kinds of
# side-channel attacks/characterization)
#
# The traces used in this example will be simulated using the
# BasicAesSimulationContainer.

from lascar import BasicAesSimulationContainer

container = BasicAesSimulationContainer(500, noise=1)

# Then, there are three requirements for instanciating the `DpaEngine`:
# - a name for the engine ("dpa" in our case)
# - a selection function (under guess hypothesis): this function will separate
#   the traces into two sets, depending on a hypothesis: "guess". This function
#   will be applied on every trace values, for every possible guess.
# - a guess_range: what are the guesses you want to test?
#
# In this example, we will focus on the LSB bit of the 3rd AES sbox. This value
# is conditioned by a single key byte (256 guesses).

from lascar.tools.aes import sbox
from lascar import DpaEngine


def selection_function(value, guess):
    return sbox[value["plaintext"][3] ^ guess] & 1


guess_range = range(256)
dpa_engine = DpaEngine("dpa", selection_function, guess_range)

# We can now create a Session, register the dpa_lsb_engine, and run it.

from lascar import Session

session = Session(container, engine=dpa_engine)
# `session.add_engine` can be used as well to add the engine

session.run(batch_size=100)  # traces loaded by batches of 100

# Now, to get the result, one solution can be to request the
# `dpa_lsb_engine.finalize()` method. As most of the engines, the `finalize()`
# method returns sca results. For more option about how to manage results of
# sca, please follow the next step of the tutorial.

results = dpa_engine.finalize()

print(results.shape)
print("Best guess is %02X." % results.max(1).argmax())

import matplotlib.pyplot as plt

plt.plot(results.T)
plt.show()