  Profiled side-channel analysis (SCA) leverages leakage from cryptographic
implementations to extract the secret key. When combined with advanced methods
in neural networks (NNs), profiled SCA can successfully attack even those
crypto-cores assumed to be protected against SCA. Despite the rise in the
number of studies devoted to NN-based SCA, a range of questions has remained
unanswered, namely: how to choose an NN with an adequate configuration, how to
tune the NN's hyperparameters, when to stop the training, etc. Our proposed
approach, ``InfoNEAT,'' tackles these issues in a natural way. InfoNEAT relies
on the concept of neural structure search, enhanced by information-theoretic
metrics to guide the evolution, halt it with novel stopping criteria, and
improve time-complexity and memory footprint. The performance of InfoNEAT is
evaluated by applying it to publicly available datasets composed of real
side-channel measurements. In addition to the considerable advantages regarding
the automated configuration of NNs, InfoNEAT demonstrates significant
improvements over other approaches for effective key recovery in terms of the
number of epochs (e.g.,x6 faster) and the number of attack traces compared to
both MLPs and CNNs (e.g., up to 1000s fewer traces to break a device) as well
as a reduction in the number of trainable parameters compared to MLPs (e.g., by
the factor of up to 32). Furthermore, through experiments, it is demonstrated
that InfoNEAT's models are robust against noise and desynchronization in
traces.
