EM-X-DL：带噪声EM签名的高效跨设备深度学习侧信道攻击
EM-X-DL: Efficient Cross-Device Deep Learning Side-Channel Attack with Noisy EM Signatures
摘要：
这项工作提出了一种基于跨设备深度学习的电磁（EM-X-DL）侧信道分析（SCA），即使在信噪比（SNR）明显低于以前的工作的情况下，也能在AES-128上实现>90%的单迹攻击准确度。通过智能选择多个训练设备和正确选择超参数，可以在8位Atmel微控制器上运行的目标加密引擎上利用PCA、LDA和FFT等预处理技术高效地训练所提出的256类深度神经网络（DNN）。最后，使用EM-X-DL的高效端到端SCA泄漏检测和攻击框架证明了攻击者具有<20个平均EM跟踪的高置信度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
