Playing with blocks: Toward re-usable deep learning models for side-channel profiled attacks. (arXiv:2203.08448v1 [cs.CR])

This paper introduces a deep learning modular network for side-channel
analysis. Our deep learning approach features the capability to exchange part
of it (modules) with others networks. We aim to introduce reusable trained
modules into side-channel analysis instead of building architectures for each
evaluation, reducing the body of work when conducting those. Our experiments
demonstrate that our architecture feasibly assesses a side-channel evaluation
suggesting that learning transferability is possible with the network we
propose in this paper.

attacks channel deep learning side-channel