Paper 2024/170 Original: eprint.iacr.org/2024/170 ↗

Train Wisely: Multifidelity Bayesian Optimization Hyperparameter Tuning in Side-Channel Analysis

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.