Abstract:
This work explores a novel method of SCA profiling to address compatibility problems and bolster Deep Learning (DL)
models. Convolutional Neural Networks are proposed in this research as a countermeasure to misalignment-focused countermeasures.
We discovered that CNNs provide the potential for end-to-end profiling attacks, where sensitive information can be directly extracted
from raw data without the need for any preprocessing. We are of the opinion that dimensionality reduction approaches and realignments
both carry the danger of erasing valuable information from data. Indeed, in order to get a well-synchronized dataset, a realignment
method modifies signals so that traces are somewhat comparable to one another. "Time-Delay Convolutional Neural Networks"
(TDCNN) is more accurate than "Convolutional Neural Network," yet it's still acceptable. It's true that TDCNNs are neural networks
based on convolution learned on single spatial information, just as side-channel tracings. However, given to recent surge in popularity
of CNNs, particularly from the year 2012 when CNN framework ("AlexNet") achieved Image Net Large Scale Visual Recognition
Competition, a notable picture detection competition, moniker TDCNN has been phased out in DL literature. Currently, one needs to
employ characteristics related to CNN design, including declaring that one input feature equals 1, for instance, to establish a TDCNN
in the most widely used DL libraries.