A Fast Characterization Method for Semi-invasive Fault Injection Attacks

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

Paper 2019/866

A Fast Characterization Method for Semi-invasive Fault Injection Attacks

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

Abstract

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.

Metadata

Available format(s): PDF
Category: Implementation
Publication info: Published elsewhere. CT-RSA
Keywords: Physical attacks Fault injection Fast space characterization Deep learning Metrics
Contact author(s): picek stjepan @ gmail com
lichao wu9 @ gmail com
gerard ribera s @ gmail com
nberinguier @ gmail com
History: 2021-02-04: last of 4 revisions; 2019-07-25: received; See all versions
Short URL: https://ia.cr/2019/866
License: CC BY

BibTeX

@misc{cryptoeprint:2019/866,
      author = {Lichao Wu and Gerard Ribera and Noemie Beringuier-Boher and Stjepan Picek},
      title = {A Fast Characterization Method for Semi-invasive Fault Injection Attacks},
      howpublished = {Cryptology ePrint Archive, Paper 2019/866},
      year = {2019},
      note = {\url{https://eprint.iacr.org/2019/866}},
      url = {https://eprint.iacr.org/2019/866}
}