Design of Time-Delay Convolutional Neural Networks(TDCNN) Model for Feature Extraction for Side-Channel Attacks
Amjed Abbas Ahmed, Mohammad Kamrul Hasan, Shahrul Azman Mohd Noah, Azana Hafizah Mohd Aman
Abstract
This work explores a novel method of SCA profiling to address compatibility problems and strengthen Deep Learning (DL) models.Convolutional Neural Networks are proposed in this research as a countermeasure to misalignment-focused countermeasures."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 which is a notable image detection competition, a novel TDCNN has been termed out in DL literature.Currently, it needs to employ the 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.