Playing With Blocks: Toward Re-Usable Deep Learning Models for Side-Channel Profiled Attacks
Paguada S., Batina L., Buhan I., Armendariz I.
IEEE Transactions on Information Forensics and Security
This paper introduces a deep learning modular network for side-channel analysis. Our approach features a deep learning architecture with the capability to exchange parts (modules) with other neural networks. We aim to introduce reusable trained modules into side-channel analysis instead of building architectures from scratch for each evaluation, reducing the body of work. Our experiments demonstrate that our architecture feasibly assesses a side-channel evaluation, suggesting that learning transferability is possible using the architecture we propose in this paper.