Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EM-X-DL: Efficient Cross-Device Deep Learning Side-Channel Attack with
  Noisy EM Signatures   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.

2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin