论文标题
玩块：朝着可重新使用的深度学习模型进行侧渠道构图攻击
Playing with blocks: Toward re-usable deep learning models for side-channel profiled attacks
论文作者
论文摘要
本文介绍了一个深度学习模块化网络，用于侧通道分析。我们的深度学习方法具有与其他网络交换部分（模块）的能力。我们旨在将可重复使用的训练的模块引入侧渠道分析，而不是为每次评估构建体系结构，从而减少进行这些评估的工作。我们的实验表明，我们的体系结构可行地评估侧向通道评估，这表明我们在本文中提出的网络可以学习可转移性。
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.
