Side-channel analysis (SCA) can obtain information related to the secret key by exploiting leakages produced by the device. Researchers recently found that neural networks (NNs) can execute a powerful profiling SCA, even on targets protected with countermeasures. This paper explores the effectiveness of Neuroevolution to Attack Side-channel Traces Yielding Convolutional Neural Networks (NASCTY-CNNs), a novel genetic algorithm approach that applies genetic operators on architectures' hyperparameters to produce CNNs for side-channel analysis automatically. The results indicate that we can achieve performance close to state-of-the-art approaches on desynchronized leakages with mask protection, demonstrating that similar neuroevolution methods provide a solid venue for further research. Finally, the commonalities among the constructed NNs provide information on how NASCTY builds effective architectures and deals with the applied countermeasures.

翻译：研究人员最近发现,神经网络(NNS)可以实施强大的剖析SCA, 即使是在受反措施保护的目标上。本文探讨了神经革命攻击侧通道反射神经网络(NASCTY-CNNs)的有效性,这是一种新型的遗传算法方法,在建筑的超参数上应用基因操作员来自动生成CNN进行侧通道分析的CNN。结果显示,我们可以在使用面具保护的脱同步渗漏方面接近最先进的方法,表明类似的神经革命方法为进一步研究提供了坚实的场所。最后,所建的NCDTY提供了如何建立有效架构和处理应用反措施的共通性信息。