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→Creating the Template: There aren't 16 bits in a byte, Greg.
One of the downsides of template attacks is that they require a great number of traces to be preprocessed before the attack can begin. This is mainly for statistical reasons. In order to come up with a good distribution to model the power traces for ''every key'', we need a large number of traces for ''every key''. For example, if we're going to attack a single subkey of AES-128, then we need to create 256 power consumption models (one for every number from 0 to 255). In order to get enough data to make good models, we need tens of thousands of traces.
Note that we don't have to model every single key. One good alternative is to model a sensitive part of the algorithm, like the substitution box in AES. We can get away with a much smaller number of traces here; if we make a model for every possible Hamming weight, then we would end up with 17 9 models, which is an order of magnitude smaller. However, then we can't recover the key from a single attack trace - we need more information to recover the secret key.
== Points of Interest ==
== Analyzing the Data ==
Suppose that we've picked <math>I</math> points of interest, which are at samples <math>s_i</math> (<math>0 \le i < I</math>). Then, our goal is to find a mean and covariance matrix for every operation (every choice of subkey or intermediate Hamming weight). Let's say that there are <math>K</math> of these operations (maybe 256 subkeys or 17 9 possible Hamming weights).
For now, we'll look at a single operation <math>k</math> (<math>0 \le k < K</math>). The steps are:
