Paper 2019/578

Deep Learning based Side Channel Attacks in Practice

Houssem Maghrebi

Abstract

A recent line of research has investigated a new profiling technique based on deep learning as an alternative to the well-known template attack. The advantage of this new profiling approach is twofold: $(1)$ the approximation of the information leakage by a multivariate Gaussian distribution is relaxed (leading to a more generic approach) and $(2)$ the pre-processing phases such as the traces realignment or the selection of the Points of Interest (PoI) are no longer mandatory, in some cases, to succeed the key recovery (leading to a less complex security evaluation roadmap). The related published works have demonstrated that Deep Learning based Side-Channel Attacks (DL-SCA) are very efficient when targeting cryptographic implementations protected with the common side-channel countermeasures such as masking, jitter and random delays insertion. In this paper, we assess the efficiency of this new profiling attack under different realistic and practical scenarios. First, we study the impact of the intrinsic characteristics of the manipulated data-set (\emph{i.e.} distance in time samples between the PoI, the dimensionality of the area of interest and the pre-processing of the data) on the robustness of the attack. We demonstrate that the deep learning techniques are sensitive to these parameters and we suggest some practical recommendations that can be followed to enhance the profiling and the key recovery phases. Second, we discuss the tolerance of DL-SCA with respect to a deviation from the idealized leakage models and provide a comparison with the well-known stochastic attack. Our results show that DL-SCA are still efficient in such a context. Then, we target a more complex masking scheme based on Shamir's secret sharing and prove that this new profiling approach is still performing well. Finally, we conduct a security evaluation of a batch of several combinations of side-channel protections using simulations and real traces captured on the ChipWhisperer board. The experimental results obtained confirm that DL-SCA are very efficient even when a cryptographic implementation combines several side-channel countermeasures.

Metadata
Available format(s)
PDF
Category
Implementation
Publication info
Preprint. MINOR revision.
Keywords
Deep Learning based Side-Channel AttacksData DimensionalityData ScalingArtificial NoiseSide-Channel CountermeasuresShamir's Secret SharingCombination of Countermeasures
Contact author(s)
houssem mag @ gmail com
History
2019-05-28: received
Short URL
https://ia.cr/2019/578
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2019/578,
      author = {Houssem Maghrebi},
      title = {Deep Learning based Side Channel Attacks in Practice},
      howpublished = {Cryptology {ePrint} Archive, Paper 2019/578},
      year = {2019},
      url = {https://eprint.iacr.org/2019/578}
}
Note: In order to protect the privacy of readers, eprint.iacr.org does not use cookies or embedded third party content.