  To make cryptographic processors more resilient against side-channel attacks,
engineers have developed various countermeasures. However, the effectiveness of
these countermeasures is often uncertain, as it depends on the complex
interplay between software and hardware. Assessing a countermeasure's
effectiveness using profiling techniques or machine learning so far requires
significant expertise and effort to be adapted to new targets which makes those
assessments expensive. We argue that including cost-effective automated attacks
will help chip design teams to quickly evaluate their countermeasures during
the development phase, paving the way to more secure chips.
  In this paper, we lay the foundations toward such automated system by
proposing GPAM, the first deep-learning system for power side-channel analysis
that generalizes across multiple cryptographic algorithms, implementations, and
side-channel countermeasures without the need for manual tuning or trace
preprocessing. We demonstrate GPAM's capability by successfully attacking four
hardened hardware-accelerated elliptic-curve digital-signature implementations.
We showcase GPAM's ability to generalize across multiple algorithms by
attacking a protected AES implementation and achieving comparable performance
to state-of-the-art attacks, but without manual trace curation and within a
limited budget. We release our data and models as an open-source contribution
to allow the community to independently replicate our results and build on
them.
