tutorial_07_dpa_example.rst

Differential Power Analysis example

Based on what's introducted in the previous pages, this tutorial will now show now to setup a classical Differential Power Attack (DPA) using Lascar.

For that, a :class:`DpaEngine <lascar.engine.dpa_engine.DpaEngine>` is instanciated and registered it to a :class:`Session <lascar.session.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 :class:`BasicAesSimulationContainer <lascar.container.simulation_container.BasicAesSimulationContainer>`:

from lascar import BasicAesSimulationContainer

container = BasicAesSimulationContainer(500, noise=1)

Then, there are three requirements for instanciating the :class:`DpaEngine <lascar.engine.dpa_engine.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 :class:`Session <lascar.session.Session>`, register the dpa_lsb_engine, and run it:

from lascar import Session

session = Session(container, engine=dpa_engine)
session.run(batch_size=100)  # traces loaded by batches of 100

Note that the :meth:`Session.add_engine() <lascar.session.Session.add_engine>` method can be used as well to register the engine to the session.

Finally, to get the result, one solution can be to request the :meth:`dpa_lsb_engine.finalize() <lascar.engine.engine.Engine.finalize>` method. As most of the engines, the :meth:`finalize() <lascar.engine.engine.Engine.finalize>` method returns side-channels analysis results. For more option about how to manage the results, 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()

This will print the best guess and plot the measurements:

Session |100%|####|500 trc/500 | (3 engines, batch_size=100, leakage_shape=(26,)) |Time:  0:00:01
(256, 26)
Best guess is 03.

Guesses correlation