Correlation-Power-Analysis

• A Python Class including codes and methods to perform statistical correlation power analysis on AES-128 Encryption
• Uses Pearson Correlation Coefficient

Dependencies

• numpy
• Pandas
• Matplotlib

User Instructions and project Initialization

• make sure that the Numpy, Pandas, and Matplotlib packages are already installed on your machine.
• make sure that the CorrelationPowerAnalysis.py file, your main.py file, your power-samples CSV file, and input plaintexts CSV files are in the same directory.
• In the example project,the input plaintexts and their equivalent power samples are stored in different CSV files.
• Simply import your desired class from CorrelationPowerAnalysis.py in your main project as well as Numpy, matplotlib, and pandas.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from CorrelationPowerAnalysis import CpaOnAES128 as CPA

• In the example project, we use pandas to read the input plaintexts and their equivalent power samples.

plain = pd.read_csv("plain.csv").values
powerTraces = pd.read_csv("power.csv").values

• Create an instance from your desired class.

cpaOBJ = CPA()

• Using .SetPlainTexts() and .SetPowerTraces() methods, we assign our measurements and plaintexts to numpy arrays in the class.

cpaOBJ.SetPlainTexts(plain)
cpaOBJ.SetPowerTraces(powerTraces)

• Now you are able to run different methods of CPA based on what you need.

Different CPA Methods

CpaOnFirstKeyByte():

Performs CPA on the first key byte of the AES-128 and plots the final correlation values for each key hypothesis.

cpaOBJ.CpaOnFirstKeyByte()

CpaOnDesiredKeyByte(keyByteNum):

Performs CPA on a specified key byte of the AES-128 and plots the final correlation values for each key hypothesis.

cpaOBJ.CpaOnDesiredKeyByte(4) # In this example, CPA attacks on the 4th key byte

GradualCpaOnFirstKeyByte(stepSize):

Performs CPA with gradually increasing data-set sizes (which are generated automatically) on the first key byte of the AES-128 and plots the final correlation values for each key hypothesis as well as a plot for correlation values for different number of traces.

cpaOBJ.GradualCpaOnFirstKeyByte(1000) # In this example, CPA attack will be performed on
                                      # the data-set sizes which are integer multiples of 1000 (1000,2000,...)

GradualCpaOnDesiredKeyByte(keyByteNum, stepSize):

Performs CPA with gradually increasing data-set sizes (which are generated automatically) on a specified key byte of the AES-128 and plots the final correlation values for each key hypothesis as well as a plot for correlation values for different number of traces.

cpaOBJ.GradualCpaOnDesiredKeyByte(4,1000) # In this example, CPA attack will be performed on the 4th key byte
                                      # with the data-set sizes which are integer multiples of 1000 (1000,2000,...)