Unleashing the Power of Convolutional Neural Networks for Profiled Side-Channel Attacks

Explore a conference talk delving into the application of Convolutional Neural Networks (CNNs) for profiled side-channel attacks. Learn about the motivation behind this approach, understand the setting for template attacks, and gain insights into deep learning principles. Discover the universal approximation theorem and its relevance to the topic. Examine the design principles and architecture of CNNs for side-channel attacks, and understand how noise and data augmentation impact the results. Follow along with a practical example and draw conclusions from the research findings. Engage with the presenters during the question-and-answer session to deepen your understanding of this cutting-edge cryptographic research.

Introduction
Motivation
Setting
Template Attack
Deep Learning
Universal approximation theorem
Design principle
Architecture
Noise
Data augmentation
Results
Example
Conclusions
Questions