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.
Original language | English |
---|---|
Title of host publication | Topics in Cryptology – CT-RSA 2020 - The Cryptographers Track at the RSA Conference 2020, Proceedings |
Editors | Stanislaw Jarecki |
Publisher | SpringerOpen |
Pages | 146-170 |
Number of pages | 25 |
ISBN (Print) | 9783030401856 |
DOIs | |
Publication status | Published - 2020 |
Event | Cryptographers Track at the RSA Conference, CT-RSA 2020 - San Francisco, United States Duration: 24 Feb 2020 → 28 Feb 2020 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|
Volume | 12006 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | Cryptographers Track at the RSA Conference, CT-RSA 2020 |
---|---|
Country/Territory | United States |
City | San Francisco |
Period | 24/02/20 → 28/02/20 |
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-careOtherwise 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
- Fast space characterization
- Fault injection
- Metrics
- Physical attacks