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：侧信道分析（SCA）可通过利用设备产生的泄露获取与密钥相关的信息。研究人员近期发现，神经网络（NN）能够执行强大的建模SCA，甚至对受防护措施保护的目标也能生效。本文探索了利用神经进化攻击侧信道轨迹生成卷积神经网络（NASCTY-CNNs）的有效性，这是一种新型遗传算法方法，通过对架构超参数应用遗传算子自动生成用于侧信道分析的CNN。结果表明，在具有掩码保护的失同步泄露场景中，我们能够获得接近现有最优方法的性能，证明类似的神经进化方法为后续研究提供了坚实的方向。最后，所构建NN之间的共性揭示了NASCTY如何构建有效架构并处理所应用的防护措施。