Abstract
Blockchain, designed with cryptographic technology, is widely used in the financial area, such as digital billing and cross-border payments. Digital signature is the core technology in it. However, digital signatures in public key cryptosystems face the threat of simple power analysis in Side-Channel Analysis (SCA). The state-of-the-art simple power analysis based on clustering mostly will appear outliers in the process of analysis, which will reduce success rate of key recover. In this paper, we propose a new SCA method with clustering algorithm Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and deep learning technology Convolutional Neural Network (CNN), called DBSCAN-CNN, to analyze public key cryptosystems. We cluster data with DBSCAN firstly. Then we train a CNN model based on the trusted clustering results. Finally, we classify the outliers of clustering results by the trained model. We mount the proposed method to analyze an FPGA-based elliptic curve scalar multiplication power trace which is desynchronized by simulating random delay. The experimental results show that the error rate of the proposed method is at least \(69.23\%\) lower than that of the classical clustering method in SCA.
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Acknowledgement
This work is supported by National Key R &D Program of China (Nos. 2022YFB310 3800, 2021YFB3101500), National Natural Science Foundation of China (Nos. 62272047, 62002021), Beijing Institute of Technology Research Fund Program for Young Scholars, and Cryptographic Application Industry Chain Supply and Demand Docking Platform of New Energy and Intelligent Connected Vehicle Industry (2021-0181-1-1).
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Wang, A., He, S., Wei, C., Sun, S., Ding, Y., Wang, J. (2023). Using Convolutional Neural Network to Redress Outliers in Clustering Based Side-Channel Analysis on Cryptosystem. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_34
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DOI: https://doi.org/10.1007/978-3-031-28124-2_34
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