Profiling in side-channel cryptanalysis is a powerful tool to assess the resistance of embedded cryptographic implementations and therefore embodies a powerful attack. The adversary has to have access to a second device, similar to the device under test, that he fully controls to conduct a profiling of physically observable information about secret internals. By knowledge of implementation details the adversary is able to nearly defeat any countermeasure, at least those of algorithmic nature. To date there exist different branches, including template attacks, stochastic model, and machine learning based approaches.
In this work we propose a new attack called leakage prototype learning that aims for determining unbiased side-channel leakages instead of estimating them. Furthermore, it encompasses the locating, respectively selection, of leakage dependent time-instants with clear criteria. For one thing we provide a deep theoretical analysis by discussing mathematical foundations and properties, and for another thing a thorough analysis by practical means including performance comparisons to meaningful variants of several common profiled side-channel attacks.