Over the past few years, deep learning has been getting progressively more popular for the exploitation of side-channel vulnerabilities in embedded cryptographic applications, as it offers advantages in terms of the amount of attack traces required for effective key recovery. A number of effective attacks using neural networks have already been published, but reducing their cost in terms of the amount of computing resources and data required is an ever-present goal, which we pursue in this work. We focus on the ANSSI Side-Channel Attack Database (ASCAD), and produce a JAX-based framework for deep-learning-based SCA, with which we reproduce a selection of previous results and build upon them in an attempt to improve their performance. We also investigate the effectiveness of various Transformer-based models.

翻译：过去几年间，深度学习在嵌入式密码应用中利用侧信道漏洞方面日益普及，因为其在有效密钥恢复所需的攻击迹线数量方面具有优势。目前已有多种基于神经网络的攻击方法被公布，但如何降低这些方法对计算资源和数据量的需求始终是我们追求的目标。本研究聚焦于ANSSI侧信道攻击数据库（ASCAD），构建了一个基于JAX的深度学习侧信道攻击框架。借助该框架，我们复现了部分已有研究成果，并在此基础上尝试提升其性能。此外，我们还探讨了多种基于Transformer模型的效能表现。