Learning When to Stop: A Mutual Information Approach to Prevent Overfitting in Profiled Side-Channel Analysis

Guilherme Perin, Ileana Buhan, Stjepan Picek*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Today, deep neural networks are a common choice for conducting the profiled side-channel analysis. Unfortunately, it is not trivial to find neural network hyperparameters that would result in top-performing attacks. The hyperparameter leading the training process is the number of epochs during which the training happens. If the training is too short, the network does not reach its full capacity, while if the training is too long, the network overfits and cannot generalize to unseen examples. In this paper, we tackle the problem of determining the correct epoch to stop the training in the deep learning-based side-channel analysis. We demonstrate that the amount of information, or, more precisely, mutual information transferred to the output layer, can be measured and used as a reference metric to determine the epoch at which the network offers optimal generalization. To validate the proposed methodology, we provide extensive experimental results.

Original languageEnglish
Title of host publicationConstructive Side-Channel Analysis and Secure Design - 12th International Workshop, COSADE 2021, Proceedings
EditorsShivam Bhasin, Fabrizio De Santis
PublisherSpringer
Pages53-81
Number of pages29
ISBN (Print)9783030899141
DOIs
Publication statusPublished - 2021
Event12th International Workshop on Constructive Side-Channel Analysis and Secure Design, COSADE 2021 - Virtual, Online
Duration: 25 Oct 202127 Oct 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12910 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Workshop on Constructive Side-Channel Analysis and Secure Design, COSADE 2021
CityVirtual, Online
Period25/10/2127/10/21

Keywords

  • Information bottleneck
  • Mutual information
  • Neural networks
  • Overfitting
  • Side-channel analysis

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