Focus is Key to Success: A Focal Loss Function for Deep Learning-Based Side-Channel Analysis

Maikel Kerkhof, Lichao Wu, Guilherme Perin, Stjepan Picek*

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

The deep learning-based side-channel analysis represents one of the most powerful side-channel attack approaches. Thanks to its capability in dealing with raw features and countermeasures, it becomes the de facto standard approach for the SCA community. The recent works significantly improved the deep learning-based attacks from various perspectives, like hyperparameter tuning, design guidelines, or custom neural network architecture elements. Still, insufficient attention has been given to the core of the learning process - the loss function. This paper analyzes the limitations of the existing loss functions and then proposes a novel side-channel analysis-optimized loss function: Focal Loss Ratio (FLR), to cope with the identified drawbacks observed in other loss functions. To validate our design, we 1) conduct a thorough experimental study considering various scenarios (datasets, leakage models, neural network architectures) and 2) compare with other loss functions used in the deep learning-based side-channel analysis (both “traditional” ones and those designed for side-channel analysis). Our results show that FLR loss outperforms other loss functions in various conditions while not having computational overhead like some recent loss function proposals.

Original languageEnglish
Title of host publicationConstructive Side-Channel Analysis and Secure Design - 13th International Workshop, COSADE 2022, Proceedings
EditorsJosep Balasch, Colin O’Flynn
PublisherSpringer Science and Business Media Deutschland GmbH
Pages29-48
Number of pages20
Volume13211
ISBN (Print)9783030997656
DOIs
Publication statusPublished - 2022
Event13th International Workshop on Constructive Side-Channel Analysis and Secure Design, COSADE 2022 - Leuven, Belgium
Duration: 11 Apr 202212 Apr 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13211 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Workshop on Constructive Side-Channel Analysis and Secure Design, COSADE 2022
Country/TerritoryBelgium
CityLeuven
Period11/04/2212/04/22

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Deep learning
  • Focal loss
  • Loss function
  • Side-channel analysis

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