A fast characterization method for semi-invasive fault injection attacks

Lichao Wu, Gerard Ribera, Noemie Beringuier-Boher, Stjepan Picek

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

2 Citations (Scopus)
38 Downloads (Pure)


Semi-invasive fault injection attacks are powerful techniques well-known by attackers and secure embedded system designers. When performing such attacks, the selection of the fault injection parameters is of utmost importance and usually based on the experience of the attacker. Surprisingly, there exists no formal and general approach to characterize the target behavior under attack. In this work, we present a novel methodology to perform a fast characterization of the fault injection impact on a target, depending on the possible attack parameters. We experimentally show our methodology to be a successful one when targeting different algorithms such as DES and AES encryption and then extend to the full characterization with the help of deep learning. Finally, we show how the characterization results are transferable between different targets.

Original languageEnglish
Title of host publicationTopics in Cryptology – CT-RSA 2020 - The Cryptographers Track at the RSA Conference 2020, Proceedings
EditorsStanislaw Jarecki
PublisherSpringer Open
Number of pages25
ISBN (Print)9783030401856
Publication statusPublished - 2020
EventCryptographers Track at the RSA Conference, CT-RSA 2020 - San Francisco, United States
Duration: 24 Feb 202028 Feb 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12006 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceCryptographers Track at the RSA Conference, CT-RSA 2020
CountryUnited States
CitySan Francisco


  • Deep learning
  • Fast space characterization
  • Fault injection
  • Metrics
  • Physical attacks

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