Paper 2024/170
Train Wisely: Multifidelity Bayesian Optimization Hyperparameter Tuning in Side-Channel Analysis
, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Temasek Laboratories, Nanyang Technological University, Singapore, Temasek Laboratories, Nanyang Technological University, Singapore, Radboud University, Nijmegen, NetherlandsAbstract
Side-Channel Analysis (SCA) is critical in evaluating the security of cryptographic implementations. The search for hyperparameters poses a significant challenge, especially when resources are limited. In this work, we explore the efficacy of a multifidelity optimization technique known as BOHB in SCA. In addition, we proposed a new objective function called $ge_{+ntge}$, which could be incorporated into any Bayesian Optimization used in SCA. We show the capabilities of both BOHB and $ge_{+ntge}$ on four different public datasets. Specifically, BOHB could obtain the least number of traces in CTF2018 when trained in the Hamming weight and identity leakage model. Notably, this marks the first reported successful recovery of the key for the identity leakage model in CTF2018.
Metadata
- Available format(s)
-
PDF
- Category
- Implementation
- Publication info
- Preprint.
- Keywords
- Side-channelNeural NetworkDeep LearningProfiling attackHyperparameter Search
- Contact author(s)
-
trevor yap @ ntu edu sg
sbhasin @ ntu edu sg
l weissbart @ cs ru nl - History
- 2024-02-06: approved
- 2024-02-05: received
- See all versions
- Short URL
- https://ia.cr/2024/170
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2024/170,
author = {Trevor Yap Hong Eng and Shivam Bhasin and Léo Weissbart},
title = {Train Wisely: Multifidelity Bayesian Optimization Hyperparameter Tuning in Side-Channel Analysis},
howpublished = {Cryptology ePrint Archive, Paper 2024/170},
year = {2024},
note = {\url{https://eprint.iacr.org/2024/170}},
url = {https://eprint.iacr.org/2024/170}
}
