### Multi-Leak Deep-Learning Side-Channel Analysis

Fanliang Hu, Huanyu Wang, and Junnian Wang

##### Abstract

Deep Learning Side-Channel Attacks (DLSCAs) have become a realistic threat to implementations of cryptographic algorithms, such as Advanced Encryption Standard (AES). By utilizing deep-learning models to analyze side-channel measurements, the attacker is able to derive the secret key of the cryptographic alrgorithm. However, when traces have multiple leakage intervals for a specific attack point, the majority of existing works train neural networks on these traces directly, without a appropriate preprocess step for each leakage interval. This degenerates the quality of profiling traces due to the noise and non-primary components. In this paper, we first divide the multi-leaky traces into leakage intervals and train models on different intervals separately. Afterwards, we concatenate these neural networks to build the final network, which is called multi-input model. We test the proposed multi-input model on traces captured from STM32F3 microcontroller implementations of AES-128 and show a 2-fold improvement over the previous single-input attacks.

Available format(s)
Category
Foundations
Publication info
Published elsewhere. MINOR revision.IEEE ACCESS
DOI
10.1109/ACCESS.2022.3152831
Keywords
AESDeep learningMultiple leakageMulti-input modelSide-channel attacks
Contact author(s)
fanliang @ mail hnust edu cn
History
2022-03-06: revised
See all versions
Short URL
https://ia.cr/2021/1107

CC BY

BibTeX

@misc{cryptoeprint:2021/1107,
author = {Fanliang Hu and Huanyu Wang and Junnian Wang},
title = {Multi-Leak Deep-Learning Side-Channel Analysis},
howpublished = {Cryptology ePrint Archive, Paper 2021/1107},
year = {2021},
doi = {10.1109/ACCESS.2022.3152831},
note = {\url{https://eprint.iacr.org/2021/1107}},
url = {https://eprint.iacr.org/2021/1107}
}

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