Paper 2020/436
Deep Learning based Side-Channel Attack: a New Profiling Methodology based on Multi-Label Classification
Houssem Maghrebi
Abstract
Deep Learning based Side-Channel Attacks (DL-SCA) are an emerging security assessment method increasingly being adopted by the majority of certification schemes and certification bodies to assess the resistance of cryptographic implementations. The related published investigations have demonstrated that DL-SCA are very efficient when targeting cryptographic designs protected with the common side-channel countermeasures. Furthermore, these attacks allow to streamline the evaluation process as the pre-processing of the traces (\emph{e.g.} alignment, dimensionality reduction, \dots) is no longer mandatory. In practice, the DL-SCA are applied following the divide-and-conquer strategy such that the target, for the training and the attack phases, only depends on
Metadata
- Available format(s)
-
PDF
- Publication info
- Preprint. MINOR revision.
- Keywords
- Deep Learning based Side-Channel AttacksMulti-label trainingSide-Channel Countermeasures
- Contact author(s)
- houssem mag @ gmail com
- History
- 2020-04-19: received
- Short URL
- https://ia.cr/2020/436
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2020/436, author = {Houssem Maghrebi}, title = {Deep Learning based Side-Channel Attack: a New Profiling Methodology based on Multi-Label Classification}, howpublished = {Cryptology {ePrint} Archive, Paper 2020/436}, year = {2020}, url = {https://eprint.iacr.org/2020/436} }