Cryptology ePrint Archive: Report 2021/1107

Multi-Leak Deep-Learning Side-Channel Analysis

Fanliang Hu and 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 preprocess step for each leakage interval. This degenerates the quality of profiling traces due to 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.

Category / Keywords: Side-channel attacks, Multiple leakage, Multi-input model, AES, Deep learning

Date: received 29 Aug 2021

Contact author: fanliang at mail hnust edu cn

Available format(s): PDF | BibTeX Citation

Version: 20210831:132502 (All versions of this report)

Short URL: ia.cr/2021/1107


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