  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.
