I Choose You

Automated Hyperparameter Tuning for Deep Learning-based Side-channel Analysis

Journal Article (2024)
Author(s)

Lichao Wu (TU Delft - Cyber Security)

Guilherme Perin (TU Delft - Cyber Security)

S. Picek (TU Delft - Cyber Security)

Research Group
Cyber Security
DOI related publication
https://doi.org/10.1109/TETC.2022.3218372
More Info
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Publication Year
2024
Language
English
Research Group
Cyber Security
Issue number
2
Volume number
12
Pages (from-to)
546-557
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Abstract

Today, the deep learning-based side-channel analysis represents a widely researched topic, with numerous results indicating the advantages of such an approach. Indeed, breaking protected implementations while not requiring complex feature selection made deep learning a preferred option for profiling side-channel analysis. Still, this does not mean it is trivial to mount a successful deep learning-based side-channel analysis. One of the biggest challenges is to find optimal hyperparameters for neural networks resulting in powerful side-channel attacks. This work proposes an automated way for deep learning hyperparameter tuning based on Bayesian optimization. We build a custom framework denoted AutoSCA supporting machine learning and side-channel metrics. Our experimental analysis shows that our framework performs well regardless of the dataset, leakage model, or neural network type. We find several neural network architectures outperforming state-of-the-art attacks. Finally, while not considered a powerful option, we observe that neural networks obtained via random search can perform well, indicating that the publicly available datasets are relatively easy to break.

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