# Playing with blocks: Toward re-usable deep learning models for side-channel profiled attacks

arXiv: https://arxiv.org/abs/2203.08448 · HTML: https://mlsanity.com/paper/2203.08448/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 16 Mar 2022.

## Builds on (most cited first, 3 of 3)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Not Just a Black Box: Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1605.01713/) (2016)
- [Being Patient and Persistent: Optimizing An Early Stopping Strategy for Deep Learning in Profiled Attacks](https://mlsanity.com/paper/2111.14416/) (2021)

## Abstract (arXiv)

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.