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BPPF: a bilinear plaintext-power fusion method for enhanced profiling side-channel analysis

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Abstract

Deep learning algorithms are increasingly employed to exploit side-channel information, such as power consumption and electromagnetic leakage from hardware devices, significantly enhancing attack capabilities. However, relying solely on power traces for side-channel information often requires adequate domain knowledge. To address this limitation, this work proposes a new attack scheme. Firstly, a Convolutional Neural Network (CNN)-based plaintext-extended bilinear feature fusion model is designed. Secondly, multi-model intermediate layers are fused and trained, yielding in the increase of the amount of effective information and generalization ability. Finally, the model is employed to predict the output probability of three public side-channel datasets (e.g. ASCAD, AES\(\_\)HD, and AES\(\_\)RD), and analyze the recovery key guessing entropy for each key to efficiently assess attack efficiency. Experimental results showcase that the plaintext-extended bilinear feature fusion model can effectively enhance the Side-Channel Attack (SCA) capabilities and prediction performance. Deploying the proposed method, the number of traces required for a successful attack on the ASCAD\(\_\)R dataset is significantly reduced to less than 914, representing an 70.5% reduction in traces compared to the network in Convolutional Neural Network-Visual Geometry Group (CNNVGG16) with plaintext, which incorporating plaintext features before the fully connected layer. Compared to existing solutions, the proposed scheme requires only 80% of the power traces for the attack mask design using only 75 epochs. As a result, the power of the proposed method is well proved through the different experiments and comparison processes.

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Data availability

No datasets were generated or analysed during the current study.

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Funding

This research 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).

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Authors and Affiliations

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

    Yezhou Zhang, Lang Li & Yu Ou

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

    Yezhou Zhang, Lang Li & Yu Ou

Authors
  1. Yezhou Zhang
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  2. Lang Li
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  3. Yu Ou
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yezhou Zhang. The first draft of the manuscript was written by Yezhou Zhang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Lang Li.

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Appendix

Appendix

The used state-of-the-art models are listed in Table 3. The convolution layer is denoted by Conv; averaging pooling layer is denoted by Pool. BF denotes the fusion layer. FLAT and FC denote the flatten layer and fully connected, respectively. Finally, LSM denotes the output layer with the logsoftmax activation function.

Table 3 CNN\(\_\)BD architecture used for the attack
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Zhang, Y., Li, L. & Ou, Y. BPPF: a bilinear plaintext-power fusion method for enhanced profiling side-channel analysis. Cluster Comput 28, 2 (2025). https://doi.org/10.1007/s10586-024-04701-2

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