forked from Ledger-Donjon/lascar
-
Notifications
You must be signed in to change notification settings - Fork 1
/
snr.py
45 lines (29 loc) · 1.95 KB
/
snr.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
"""
In this script, we show how to perform side-channel characterisation with lascar to study the behaviour of an Aes Sbox
The characterisation is made with SnrEngine or NicvEngine, which role is the same.
Specifically, each of them need:
- a partition function, which will separate leakages into classs
- a "number_of_partitions" to indicate the number of possible classes for the partition_function
"""
import matplotlib.pyplot as plt
from lascar import *
container = BasicAesSimulationContainer(1000, noise=3) # We use the BasicAesSimulationContainer with 2000 traces
def partition_function(value): # partition_function must take 1 argument: the value returned by the container at each trace
return value['plaintext'][3] # here we partition on the value of the 3rd plaintext byte
number_of_partitions = 256 # number of possible classes (~output of the partiton_function) for the partition_function
snr_engine = SnrEngine("snr_plaintext_3", partition_function, range(number_of_partitions))
# We choose here to plot the resulting curve
session = Session(container, engine=snr_engine, output_method=MatPlotLibOutputMethod(snr_engine))
session.run(batch_size=500)
"""
Now let's compute the 16 snr of the 16 bytes in //
We choose here to display the 16 curves on the same plot
"""
def get_partition_function(byte):
def partition_function(value): # partition_function must take 1 argument: the value returned by the container at each trace
return value['plaintext'][byte] # here we partition on the value of the 3rd plaintext byte
return partition_function
number_of_partitions = 256 # number of possible classes (~output of the partiton_function) for the partition_function
snr_engines = [ SnrEngine("snr_plaintext_%d"%i, get_partition_function(i), range(number_of_partitions)) for i in range(16)]
session = Session(container, engines=snr_engines, output_method=MatPlotLibOutputMethod(*snr_engines, single_plot=True, legend=True))
session.run(batch_size=500)