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Side-channel analysis based on Siamese neural network

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Abstract

In recent years, the combination of deep learning and side-channel analysis has received extensive attention. Previous research has shown that the key recovery problem can be transformed into a classification problem. The performance of these models strongly depends on the size of the dataset and the number of instances in each target class. The training time is very long. In this paper, the key recovery problem is transformed into a similarity measurement problem in Siamese neural networks. We use simulated power traces and true power traces to form power pairs to augment data and simplify key recovery steps. The trace pairs are selected based on labels and added to the training to improve model performance. The model adopts a Siamese, CNN-based architecture, and it can evaluate the similarity between the inputs. The correct key is revealed by the similarity of different trace pairs. In experiments, three datasets are used to evaluate our method. The results show that the proposed method can be successfully trained with 1000 power traces and has excellent attack efficiency and training speed.

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Acknowledgements

We are grateful to the anonymous reviewers for their insightful comments. This work is supported by the Hunan Provincial Natural Science Foundation of China (2022JJ30103), “the 14th Five-Year Plan” Key Disciplines and Application-oriented Special Disciplines of Hunan Province (Xiangjiaotong [2022] 351), the Science and Technology Innovation Program of Hunan Province (2016TP1020), Open fund project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang Normal University (2022HSKFJJ011, 2021HSKFJJ038).

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Authors and Affiliations

  1. College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China

    Di Li, Lang Li & Yu Ou

  2. Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China

    Di Li, Lang Li & Yu Ou

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  1. Di Li
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  2. Lang Li
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  3. Yu Ou
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Contributions

DL helped in conceptualization and writing—original draft. LL was involved in writing—review and editing and supervision. YO contributed to term, project administration, formal analysis.

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Correspondence to Lang Li.

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Li, D., Li, L. & Ou, Y. Side-channel analysis based on Siamese neural network. J Supercomput 80, 4423–4450 (2024). https://doi.org/10.1007/s11227-023-05631-3

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