Hardware-based cryptographic implementations utilize countermeasures to resist side-channel attacks. In this paper, we propose a novel deep-learning architecture for side-channel analysis called SCANET that generalizes across multiple implementations and algorithms without manual tuning or trace pre-processing. We achieve this by combining a novel input processing technique with several advanced deep learning techniques including transformer blocks and multi-task learning. We demonstrate the generality of our approach by successfully attacking four hardware-accelerated countermeasures for elliptic curve digital signatures in an end-to-end manner without human tuning. Additionally, we showcase SCANET's ability to generalize across multiple algorithms by successfully replicating state-of-the-art attacks against protected AES without the need for trace preprocessing, hand-tuning, or model architectural changes. These results offer promising prospects for generic and automated side-channel leakage evaluation without manual effort.

翻译：基于硬件的密码实现通过采用防御措施来抵抗侧信道攻击。本文提出一种名为SCANET的新型深度学习架构用于侧信道分析，该架构无需手动调参或迹线预处理即可泛化至多种实现与算法。通过结合新型输入处理技术与若干先进深度学习技术（包括Transformer模块和多任务学习），我们实现了这一目标。我们以端到端方式成功攻克了四种采用硬件加速防护机制的椭圆曲线数字签名实现（无需人工调参），从而验证了本方法的通用性。此外，我们无需迹线预处理、手动调参或修改模型架构，即成功复现了针对带防护AES的最佳攻击效果，进一步展示了SCANET跨算法泛化的能力。这些结果为实现无需人工干预的通用自动化侧信道泄漏评估提供了极具前景的方案。