This tutorial is a more hands-on version of the previous tutorial. Rather than getting the ChipWhisperer Analyzer software to generate the points of interest and the template distributions, this tutorial will work directly with the recorded trace data in Python.

It is highly recommended that you read the theory page on Template Attacks before attempting this tutorial. There is some relatively complex processing involved, and it may be helpful to get a mathematical view on the steps before attempting to program them.

Additionally, this tutorial uses some terminology from previous tutorials, such as Hamming weight and substitution box. If you don't know what these are, Tutorial B6 Breaking AES (Manual CPA Attack) might be an easier starting point.

Capturing the Traces

As in the previous tutorial, this tutorial requires two sets of traces. The first set is a large number of traces (1000+) with random keys and plaintexts, assumed to come from your personal copy of the device. The second is a smaller number of traces (~50) with a fixed key and random plaintexts, assumed to come from the sensitive device that we're attacking. The goal of this tutorial is to recover the fixed key from the smaller set of traces.

The data collected from the previous tutorial will be fine for these steps. These examples will work with 2000 random-key traces and 50 fixed-key traces. If you don't have these datasets, follow the steps in Tutorial B7 Profiling Attacks (with HW Assumption) to record these traces.

Note that this tutorial will explain how to attack a single byte of the secret AES key. It would be easy to extend this to the full key by running the code 16 times. The smaller attack is used to make some of the code easier to grasp and debug.

Creating the Template

This section describes how to generate a template from the random-key traces. Our template will attempt to recognize the Hamming weight of the AES substitution box output. This choice of attack point limits our template - we will not be able to find the secret key in one attack trace - but it allows us to use a smaller amount of preprocessing. A more robust template attack (for instance, creating a template for each of the 256 possible key bytes) would require at least 10 times more data.

Loading the Traces

The traces recorded from the ChipWhisperer Capture program are saved as NumPy arrays. These files can be loaded using the np.load() function. We're interested in the traces, plaintext, and random keys used in our template captures, so we can load this data with the code:

import numpy as np
tempTraces = np.load(r'C:\chipwhisperer\software\temp_attack\rand_key_data\traces\2016.05.24-12.53.15_traces.npy')
tempPText  = np.load(r'C:\chipwhisperer\software\temp_attack\rand_key_data\traces\2016.05.24-12.53.15_textin.npy')
tempKey    = np.load(r'C:\chipwhisperer\software\temp_attack\rand_key_data\traces\2016.05.24-12.53.15_keylist.npy')

(Of course, fill in your own filenames.)

We can check this data to make sure it looks okay. Some useful checks might be:

import matplotlib.pyplot as plt
print tempPText
print len(tempPText)
plt.plot(tempTraces[0])
plt.show()

Get in the habit of checking your data with some basic print statements or plots - it's a good sanity check to make sure that your arrays are the size they should be! If everything looks okay, comment out these checks and move on.