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ESRM: an efficient regression model based on random kernels for side channel analysis

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Abstract

Many researches transform the traditional side channel analysis (SCA) into a classification problem. However, there are some inconsistencies in the evaluation metrics and excessive training overhead. A regression model theory is proposed from power traces to intermediate values in this work. It leads us to design a random convolution model that can closely fit the timing features of power consumption and transform them directly to intermediate values. In training phase, the raw power traces on ASCAD is processed to the dataset with six subsets, which is similar to the form of UCR sets. The determination coefficient (\(R^2\)), time and correlation coefficient are used in training and evaluation. The experiments show that the model has a faster training speed and better attack effect. Our model can address two problems in combining deep learning with SCA. Further, the model can quickly adapt to new cryptographic algorithms by greatly reducing the training time.

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Acknowledgements

This research is supported by Scientific Research Fund of Hunan Provincial Education Department with Grant No.19A072, Innovation Platform open Fund of Hengyang Normal University with Grant 2021HSKFJJ038, the science and technology innovation Program of Hunan Province (2016TP1020), Application-oriented Special Disciplines, Double First-Class University Project of Hunan Province (Xiangjiaotong [2018] 469).

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Authors and Affiliations

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

    Yu Ou, Lang Li, Di Li & Jian Zhang

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

    Yu Ou, Lang Li, Di Li & Jian Zhang

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  1. Yu Ou
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  2. Lang Li
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  4. Jian Zhang
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Correspondence to Lang Li.

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Ou, Y., Li, L., Li, D. et al. ESRM: an efficient regression model based on random kernels for side channel analysis. Int. J. Mach. Learn. & Cyber. 13, 3199–3209 (2022). https://doi.org/10.1007/s13042-022-01588-6

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  • DOI: https://doi.org/10.1007/s13042-022-01588-6

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