BH18: Lowering the Bar: Deep Learning for Side Channel Analysis

Jasper van Woudenberg, Riscure

The old way of doing side channel analysis was to do leakage modeling to pull out keys from the signals. Started researching what happens if they use a neural network for the analysis.

They still need to attach the scopes and wires to the device, can't get robots to do that, yet. They do several runs and look for variations in signal/power usage to find leakages from the patterns (and divergence of the patterns).

Then we got a demo of some signal analysis - he made a mistake, and noted that is the problems with humans, we make mistakes.

Understanding the power consumption can give you the results of X (X-or of Input and Key), then if we know input - we can get the key! Still a lot of work to do.

In template analysis, you build models around various devices from power traces - then look for other devices using the same chipset, and then can start gathering input for analysis.

The researchers than looked at improving their processes with Convolutional Neural Networks (CNNS). THere is the input layer (size is equal to number of samples), the convolutional layer (feature extractor + encoding), then Dense Layers (classifiers) and finally the output later. Convolutional layers are able to detect the features independently of their positions.

There are a lot of visuals and live tracing, hard to capture here, but fascinating to watch :-)

Caveat - don't give too much input, make the network is too big = or the model cannot actually learn and will not be able classify new things.  (memorizes vs learning).  Need to verify this with validation recall. 

Deep learning can really help with side channel analysis and it scales well. It does require network fiddling, but it's not that hard. This automation will help put a dent into better securing embedded devices.