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→Performing the Attack: Added section
= Performing the Attack =
== Loading the Traces ==
If you followed the instructions from the previous tutorial, you'll have a few dozen traces that use random plaintexts and a fixed key. We can load these in exactly the same way as the template traces:
<pre>
atkTraces = np.load(r'C:\chipwhisperer\software\temp_attack\fixed_key_data\traces\2016.05.24-12.10.07_traces.npy')
atkPText = np.load(r'C:\chipwhisperer\software\temp_attack\fixed_key_data\traces\2016.05.24-12.10.07_textin.npy')
atkKey = np.load(r'C:\chipwhisperer\software\temp_attack\fixed_key_data\traces\2016.05.24-12.10.07_knownkey.npy')
</pre>
In particular, let's check the key to make sure we know what our goal is:
<pre>
print atkKey
</pre>
With the default key in ChipWhisperer Capture, this prints
<pre>
[ 43 126 21 22 40 174 210 166 171 247 21 136 9 207 79 60]
</pre>
We're only attacking the first subkey, so let's see if we can get <code>43</code> to come up as the best guess.
== Using the Template ==
The very last step is to apply our template to these traces. We want to keep a running total of <math>\log P_k = \sum_j \log p_{k,j}</math>, so we'll make space for our 256 guesses:
<pre>
P_k = np.zeros(256)
</pre>
Then, we want to do the following for every attack trace:
* Grab the samples from the points of interest and store them in a list <math>\mathbf{a}</math>
* For all 256 of the subkey guesses...
** Figure out which Hamming weight we need, according to our known plaintext and guessed subkey
** Build a <code>multivariate_normal</code> object using the relevant mean and covariance matrices
** Calculate the log of the PDF (<math>\log f(\mathbf{a})</math>) and add it to the running total
* List the best guesses we've seen so far
Our implementation is:
<pre>
for j in range(len(atkTraces)):
# Grab key points and put them in a matrix
a = [atkTraces[j][POIs[i]] for i in range(len(POIs))]
# Test each key
for k in range(256):
# Find HW coming out of sbox
HW = hw[sbox[atkPText[j][0] ^ k]]
# Find p_{k,j}
rv = multivariate_normal(meanMatrix[HW], covMatrix[HW])
p_kj = rv.pdf(a)
# Add it to running total
P_k[k] += np.log(p_kj)
# Print our top 5 results so far
# Best match on the right
print P_k.argsort()[-5:]
</pre>
The output that appears is:
<pre>
[219 145 181 134 127]
[ 37 43 76 123 235]
[32 44 45 77 43]
[199 45 44 77 43]
[139 37 42 77 43]
[45 77 37 42 43]
[235 37 45 42 43]
[ 37 77 139 235 43]
</pre>
This means:
* After 1 trace, <code>43</code> wasn't even in the top 5 guesses. This is fine - there are probably a lot of key candidates tied for first place.
* After 2 traces, <code>43</code> was the 4th best guess - pretty good...
* After 3+ traces, <code>43</code> was the best candidate for the subkey.
so we successfully attacked the first subkey in 3 traces! This is a bit lucky - most of the subkeys tend to take a bit more data. Don't be surprised if your attack takes closer to a dozen trials.
= Gotchas =