Welcome to the resource topic for 2024/170
Title:
Train Wisely: Multifidelity Bayesian Optimization Hyperparameter Tuning in Side-Channel Analysis
Authors: Trevor Yap Hong Eng, Shivam Bhasin, Léo Weissbart
Abstract: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.
ePrint: https://eprint.iacr.org/2024/170
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