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Template Attacks

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Template attacks are a powerful type of side-channel attack. These attacks are a subset of profiling attacks, where an attacker creates a "profile" of a sensitive device and applies this profile to quickly find a victim's secret key.

Template attacks require more setup than CPA attacks. To perform a template attack, the attacker must have access to another copy of the protected device that they can fully control. Then, they must perform a great deal of pre-processing to create the template - in practice, this may take dozens of thousands of power traces. However, the advantages are that template attacks require a very small number of traces from the victim to complete the attack. With enough pre-processing, the key may be able to be recovered from just a single trace.

There are four steps to a template attack:

  1. Using a copy of the protected device, record a large number of power traces using many different inputs (plaintexts and keys). Ensure that enough traces are recorded to give us information about each subkey value.
  2. Create a template of the device's operation. This template notes a few "points of interest" in the power traces and a multivariate distribution of the power traces at each point.
  3. On the victim device, record a small number of power traces. Use multiple plaintexts. (We have no control over the secret key, which is fixed.)
  4. Apply the template to the attack traces. For each subkey, track which value is most likely to be the correct subkey. Continue until the key has been recovered.

Signals, Noise, and Statistics

Before looking at the details of the template attack, it is important to understand the statistics concepts that are involved. A template is effectively a multivariate distribution that describes several key samples in the power traces. This section will describe what a multivariate distribution is and how it can be used in this context.

Noise Distributions

Electrical signals are inherently noisy. Any time we take a voltage measurement, we don't expect to see a perfect, constant level. For example, if we attached a multimeter to a 5 V source and took 4 measurements, we might expect to see a data set like (4.95, 5.01, 5.06, 4.98). One way of modelling this voltage source is

\mathbf{X} = X_{actual} + \mathbf{N}

where X_{actual} is the noise-free level and \mathbf{N} is the additional noise. In our example, X_{actual} would be exactly 5 V. Then, N is a random variable: every time we take a measurement, we can expect to see a different value. Note that \mathbf{X} and \mathbf{N} are bolded to show that they are random variables.

A simple model for these random variables uses a Gaussian distribution (read: a bell curve). The probability density function (PDF) of a Gaussian distribution is

f(x) = \frac{1}{\sigma \sqrt{2\pi}} e^{-(x - \mu)^2 / 2\sigma^2}

where \mu is the mean and \sigma is the standard deviation. For instance, our voltage source might have a mean of 5 and a standard deviation of 0.5, making the PDF look like:


We can use the PDF to calculate how likely a certain measurement is. Using this distribution,

f(5.1) \approx 0.7821

f(7.0) \approx 0.0003

so we're very unlikely to see a reading of 7 V. We'll use this to our advantage in this attack: if f(x) is very small for one of our subkey guesses, it's probably a wrong guess.

Multivariate Statistics

The 1-variable Gaussian distribution works well for one measurement. What if we're working with more than one random variable?

Suppose we're measuring two voltages that have some amount of noise on them. We'll call them \mathbf{X} and \mathbf{Y}. As a first attempt, we could write down a model for \mathbf{X} using a normal distribution and a separate model for \mathbf{Y} using a different distribution. However, this might not always make sense. If we write two separate distributions, what we're saying is that the two variables are independent: when \mathbf{X} goes up, there's no guarantee that \mathbf{Y} will follow it.

Multivariate distributions let us model multiple random variables that may or may not be correlated. In a multivariate distribution, instead of writing down a single variance \sigma, we keep track of a whole matrix of covariances. For example, to model three random variables (\mathbf{X}, \mathbf{Y}, \mathbf{Z}), this matrix would be

\mathbf{\Sigma} = 
Var(\mathbf{X})             & Cov(\mathbf{X}, \mathbf{Y}) & Cov(\mathbf{X}, \mathbf{Z}) \\
Cov(\mathbf{Y}, \mathbf{X}) & Var(\mathbf{Y})             & Cov(\mathbf{Y}, \mathbf{Z}) \\
Cov(\mathbf{Z}, \mathbf{X}) & Cov(\mathbf{Z}, \mathbf{Y}) & Var(\mathbf{Z}) 

Also, note that this distribution needs to have a mean for each random variable:

\mathbf{\mu} = 
\mu_X \\
\mu_Y \\

The PDF of this distribution is more complicated: instead of using a single number as an argument, it uses a vector with all of the variables in it (\mathbf{x} = [x, y, z, \dots]^T). The equation for k random variables is

= \frac{1}{\sqrt{(2\pi)^k |\mathbf{\Sigma}|}} 
  e^{-(\mathbf{(x - \mu)}^T \mathbf{\Sigma}^{-1} \mathbf{(x - \mu)} / 2}

Don't worry if this looks crazy - the SciPy package in Python will do all the heavy lifting for us. As with the single-variable distributions, we're going to use this to find how likely a certain observation is. In other words, if we put k points of our power trace into \mathbf{x} and we find that f(\mathbf{x}) is very high, then we've probably found a good guess.

Creating the Template

A template is a set of probability distributions that describe what the power traces look like for many different keys. Effectively, a template says: "If you're going to use key k, your power trace will look like the distribution f_k(\mathbf{x})". We can use this information to find subtle differences between power traces and to make very good key guesses for a single power trace.

Number of Traces

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 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

Our goal is to create a multivariate probability describing the power traces for every possible key. If we modeled the entire power trace this way (with, say, 3000 samples), then we would need a 3000-dimension distribution. This is insane, so we'll find an alternative.

Thankfully, not every point on the power trace is important to us. There are two main reasons for this:

  • We might be taking more than one sample per clock cycle. (Through most of the ChipWhisperer tutorials, our ADC runs four times faster than the target device.) There's no real reason to use all of these samples - we can get just as much information from a single sample at the right time.
  • Our choice of key doesn't affect the entire power trace. It's likely that the subkeys only influence the power consumption at a few critical times. If we can pick these important times, then we can ignore most of the samples.

These two points mean that we can usually live with a handful (3-5) of points of interest. If we can pick out good points and write down a model using these samples, then we can use a 3D or 5D distribution - a great improvement over the original 3000D model.

Picking POIs

There are several ways to pick the most important points in each of the traces. Generally, the aim is to find points that vary strongly between different operations (subkeys or Hamming weights). The simplest method -- the one that we'll use here -- is the sum of differences method.

The algorithm for the sum of difference method is:

  • For every operation k and every sample i, find the average power M_{k, i}. For instance, if there are T_k traces where we performed operation k, then this average is

M_{k, i} = \frac{1}{T_k} \sum_{j=1}^{T_k} t_{j, i}

  • After finding all of the means, calculate all of their absolute pairwise differences. Add these up. This will give one "trace" which has peaks where the samples are usually different. The calculation looks like

D_{i} = \sum_{k_1, k_2} |M_{k_1, i} - M_{k_2, i}|

An example of this sum of differences is:


  • The peaks of D_i show the most important points, but we need to satisfy point 1 from above - we need to pick some peaks that aren't too close. One algorithm to do this is:
  1. Pick the highest point in D_i and save this value of i as a point of interest. (ie: i = argmax(D_i))
  2. Throw out the nearest N points (where N is the minimum spacing between POIs).
  3. Repeat until enough POIs have been selected.

Analyzing the Data

Suppose that we've picked I points of interest, which are at samples s_i (0 \le i < I). 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 K of these operations (maybe 256 subkeys or 9 possible Hamming weights).

For now, we'll look at a single operation k (0 \le k < K). The steps are:

  • Find every power trace t that falls under the category of "operation k". (ex: find every power trace where we used a subkey of 0x01.) We'll say that there are T_k of these, so t_{j, s_i} means the value at trace j and POI i.
  • Find the average power \mu_i at every point of interest. This calculation will look like:

\mu_i = \frac{1}{T_k} \sum_{j=1}^{T_k} t_{j, s_i}

  • Find the variance v_i of the power at each point of interest. One way of calculating this is:

v_i = \frac{1}{T_k} \sum_{j=1}^{T_k} (t_{j, s_i} - \mu_i)^2

  • Find the covariance c_{i, i^*} between the power at every pair of POIs (i and i^*). One way of calculating this is:

c_{i, i^*} = \frac{1}{T_k}  \sum_{j=1}^{T_k} (t_{j, s_i} - \mu_i) (t_{j, s_{i^*}} - \mu_{i^*})

  • Put together the mean and covariance matrices as:

\mathbf{\mu} = 
\mu_1 \\
\mu_2 \\
\mu_3 \\

\mathbf{\Sigma} = 
v_1     & c_{1,2} & c_{1,3} & \dots \\
c_{2,1} & v_2     & c_{2,3} & \dots \\
c_{3,1} & c_{3,2} & v_3     & \dots \\
\vdots  & \vdots  & \vdots  & \ddots 

These steps must be done for every operation k. At the end of this preprocessing, we'll have K mean and covariance matrices, modelling each of the K different operations that the target can do.

Using the Template

With a template in hand, we can finish our attack. For the attack, we need a smaller number of traces - we'll say that we have A traces. The sample values will be labeled a_{j, s_i} (1 \le j \le A).

Applying the Template

First, let's apply the template to a single trace. Our job is to decide how likely all of our key guesses are. We need to do the following:

  • Put our trace values at the POIs into a vector. This vector will be

\mathbf{a_j} = 
a_{j,1} \\
a_{j,2} \\
a_{j,3} \\

  • Calculate the PDF for every key guess and save these for later. This might look like:

p_{k, j} = f_k(\mathbf{a_j})

  • Repeat these two steps for all of the attack traces.

This process gives us an array of p_{k, j}, which says: "Looking at trace j, how likely is it that key k is the correct one?"

Combining the Results

The very last step is to combine our p_{k, j} values to decide which key is the best fit. The easiest way to do this is to combine them as

P_k = \prod_{j=1}^{A} p_{k,j}

For example, if we guessed that a subkey was equal to 0x00 and our PDF results in 3 traces were (0.9, 0.8, 0.95), then our overall result would be 0.684. Having one trace that doesn't match the template can cause this number to drop quickly, helping us eliminate the wrong guesses. Finally, we can pick the highest value of P_k, which tells us which guess fits the templates the best, and we're done!

This method of combining our per-trace results can suffer from precision issues. After multiplying many large or small numbers together, we could end up with numbers that are too large or small to fit into a floating point variable. An easy fix is to work with logarithms. Instead of using P_k directly, we can calculate

\log P_k = \sum_{j=1}^A \log p_{k,j}

Comparing these logarithms will give us the same results without the precision issues.

Attack Time

The number of attack traces required to complete a template attack can vary wildly depending on the quality of the preprocessing. As described in the original papers on template attacks (Chari, Rao, and Rohatgi), when using high-quality templates made from many traces, it is possible to attack a system with a single trace. In the ChipWhisperer tutorials, we'll save ourselves a bit of processing time and only record a few thousand traces for generating the templates, meaning that we'll need more attack traces to recover the key from the victim.

It may be interesting to experiment with different amounts of processing. Can you break AES in one trace? How many template traces does it take?