Leading Degree: A Metric for Model Performance Evaluation and Hyperparameter Tuning in Deep Learning-Based Side-Channel Analysis
Abstract: Side-channel analysis benefits a lot from deep learning techniques, which assist attackers in recovering the secret key with fewer attack traces than before, but it remains a problem to precisely measure deep learning model performance, so as to obtain a high-performance model. Commonly used evaluation metrics for deep learning like accuracy and precision cannot well meet the demand due to their deviation in side-channel analysis, and classical evaluation metrics for side-channel analysis like guessing entropy, success rate and TGE1 are not generic because they effectively evaluate model performance in only one of the two situations: whether models manage to recover the secret key with given attack traces or not, and not efficient because they need to be performed multiple times to counteract randomness. To attain an effective generic side-channel evaluation metric, we investigate the deterministic component of power consumption, find that the elements of score vector under a key f.... https://tches.iacr.org/index.php/TCHES/article/view/12050
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Leading Degree: A Metric for Model Performance Evaluation and Hyperparameter Tuning in Deep Learning-Based Side-Channel Analysis ; volume:2025 ; number:2 ; year:2025
IACR transactions on cryptographic hardware and embedded systems ; 2025, Heft 2 (2025)
- Creator
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Zhu, Junfan
Lu, Jiqiang
- DOI
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10.46586/tches.v2025.i2.333-361
- URN
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urn:nbn:de:101:1-2503121800010.897870268669
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:27 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Zhu, Junfan
- Lu, Jiqiang
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