Paper 2019/1068
Not a Free Lunch but a Cheap Lunch: Experimental Results for Training Many Neural Nets Efficiently
Joey Green, Tilo Burghardt, and Elisabeth Oswald
Abstract
Neural Networks have become a much studied approach in the recent literature on profiled side channel attacks: many articles examine their use and performance in profiled single-target DPA style attacks. In this setting a single neural net is tweaked and tuned based on a training data set. The effort for this is considerable, as there a many hyper-parameters that need to be adjusted. A straightforward, but impractical, extension of such an approach to multi-target DPA style attacks requires deriving and tuning a network architecture for each individual target. Our contribution is to provide the first practical and efficient strategy for training many neural nets in the context of a multi target attack. We show how to configure a network with a set of hyper-parameters for a specific intermediate (SubBytes) that generalises well to capture the leakage of other intermediates as well. This is interesting because although we can't beat the no free lunch theorem (i.e. we find that different profiling methods excel on different intermediates), we can still get ``good value for money'' (i.e. good classification results across many intermediates with reasonable profiling effort).
Metadata
- Available format(s)
-
PDF
- Category
- Implementation
- Publication info
- Preprint. MINOR revision.
- Keywords
- AESInference Based AttacksSide Channel AttacksTemplate AttacksDeep LearningNeural Network
- Contact author(s)
- elisabeth oswald @ aau at
- History
- 2020-05-28: last of 2 revisions
- 2019-09-23: received
- See all versions
- Short URL
- https://ia.cr/2019/1068
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2019/1068,
author = {Joey Green and Tilo Burghardt and Elisabeth Oswald},
title = {Not a Free Lunch but a Cheap Lunch: Experimental Results for Training Many Neural Nets Efficiently},
howpublished = {Cryptology {ePrint} Archive, Paper 2019/1068},
year = {2019},
url = {https://eprint.iacr.org/2019/1068}
}