  Side-channel analysis (SCA) can obtain information related to the secret key
by exploiting leakages produced by the device. Researchers recently found that
neural networks (NNs) can execute a powerful profiling SCA, even on targets
protected with countermeasures. This paper explores the effectiveness of
Neuroevolution to Attack Side-channel Traces Yielding Convolutional Neural
Networks (NASCTY-CNNs), a novel genetic algorithm approach that applies genetic
operators on architectures' hyperparameters to produce CNNs for side-channel
analysis automatically. The results indicate that we can achieve performance
close to state-of-the-art approaches on desynchronized leakages with mask
protection, demonstrating that similar neuroevolution methods provide a solid
venue for further research. Finally, the commonalities among the constructed
NNs provide information on how NASCTY builds effective architectures and deals
with the applied countermeasures.
