Welcome to the resource topic for 2019/1476
Title:
On the Performance of Multilayer Perceptron in Profiling Side-channel Analysis
Authors: Leo Weissbart
Abstract:In profiling side-channel analysis, machine learning-based analysis nowadays offers the most powerful performance. This holds especially for techniques stemming from the neural network family: multilayer perceptron and convolutional neural networks. Convolutional neural networks are often favored as results suggest better performance, especially in scenarios where targets are protected with countermeasures. Multilayer perceptron receives significantly less attention, and researchers seem less interested in this method, narrowing the results in the literature to comparisons with convolutional neural networks. On the other hand, a multilayer perceptron has a much simpler structure, enabling easier hyperparameter tuning and, hopefully, contributing to the explainability of this neural network inner working. We investigate the behavior of a multilayer perceptron in the context of the side-channel analysis of AES. By exploring the sensitivity of multilayer perceptron hyperparameters over the attack’s performance, we aim to provide a better understanding of successful hyperparameters tuning and, ultimately, this algorithm’s performance. Our results show that MLP (with a proper hyperparameter tuning) can easily break implementations with a random delay or masking countermeasures. This work aims to reiterate the power of simpler neural network techniques in the profiled SCA.
ePrint: https://eprint.iacr.org/2019/1476
See all topics related to this paper.
Feel free to post resources that are related to this paper below.
Example resources include: implementations, explanation materials, talks, slides, links to previous discussions on other websites.
For more information, see the rules for Resource Topics .