论文标题
EM-X-DL：有效的跨设备深度学习侧通道攻击噪音EM签名
EM-X-DL: Efficient Cross-Device Deep Learning Side-Channel Attack with Noisy EM Signatures
论文作者
论文摘要
与先前的工作相比，这项工作提出了跨境深度学习电磁（EM-X-DL）侧通道分析（SCA），在AES-128上达到了> 90％的单轨攻击精度，即使在存在明显较低的信噪比（SNR）的情况下，与先前的工作相比。通过智能选择多种训练设备和适当的超参数选择，可以有效地培训拟议的256级深神经网络（DNN），从而有效地利用PCA，LDA（LDA）和FFT（在8位AMPATER上运行的目标加密引擎）上的预处理技术。最后，使用EM-X-DL有效的端到端SCA泄漏检测和攻击框架表明，攻击者的平均EM痕迹的信心很高。
This work presents a Cross-device Deep-Learning based Electromagnetic (EM-X-DL) side-channel analysis (SCA), achieving >90% single-trace attack accuracy on AES-128, even in the presence of significantly lower signal-to-noise ratio (SNR), compared to the previous works. With an intelligent selection of multiple training devices and proper choice of hyperparameters, the proposed 256-class deep neural network (DNN) can be trained efficiently utilizing pre-processing techniques like PCA, LDA, and FFT on the target encryption engine running on an 8-bit Atmel microcontroller. Finally, an efficient end-to-end SCA leakage detection and attack framework using EM-X-DL demonstrates high confidence of an attacker with <20 averaged EM traces.
