  Due to the constant increase and versatility of IoT devices that should keep
sensitive information private, Side-Channel Analysis (SCA) attacks on embedded
devices are gaining visibility in the industrial field. The integration and
validation of countermeasures against SCA can be an expensive and cumbersome
process, especially for the less experienced ones, and current certification
procedures require to attack the devices under test using multiple SCA
techniques and attack vectors, often implying a high degree of complexity. The
goal of this paper is to ease one of the most crucial and tedious steps of
profiling attacks i.e. the points of interest (POI) selection and hence assist
the SCA evaluation process. To this end, we introduce the usage of Estimation
of Distribution Algorithms (EDAs) in the SCA field in order to automatically
tune the point of interest selection. We showcase our approach on several
experimental use cases, including attacks on unprotected and protected AES
implementations over distinct copies of the same device, dismissing in this way
the portability issue.
