Abstract
There has been a growing interest in the side-channel analysis (SCA) field based on deep learning (DL) technology. Various DL network or model has been developed to improve the efficiency of SCA. However, few studies have investigated the impact of the different models on attack results and the exact relationship between power consumption traces and intermediate values. Based on the convolutional neural network and the autoencoder, this paper proposes a Template Analysis Pre-trained DL Classification model named TAPDC which contains three sub-networks. The TAPDC model detects the periodicity of power trace, relating power to the intermediate values and mining the deeper features by the multi-layer convolutional net. We implement the TAPDC model and compare it with two classical models in a fair experiment. The evaluative results show that the TAPDC model with autoencoder and deep convolution feature extraction structure in SCA can more effectively extract information from power consumption trace. Also, Using the classifier layer, this model links power information to the probability of intermediate value. It completes the conversion from power trace to intermediate values and greatly improves the efficiency of the power attack.
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Acknowledgements
This research was supported by the National Natural Science Foundation of China (Grant No.61572174), Hunan Province Special Funds of Central Government for Guiding Local Science and Technology Development (2018CT5001), Hunan Provincial Natural Science Foundation of China (2019JJ60004), the Scientific Research Fund of Hunan Provincial Education Department with (19A072), Subject group construction project of Hengyang Normal University (18XKQ02), Application-oriented Special Disciplines, Double First-Class University Project of Hunan Province (Xiang-jiaotong [2018] 469), the Science and Technology Plan Project of Hunan Province (2016TP1020).
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Yu Ou received a bachelor’s degree from Hengyang Normal University, Hengyang, China, in 2018. He is currently pursuing a master’s degree in the College of Information Science and Engineering, Hunan Normal University, China. His current research interests include embedded systems and information security.
Lang Li received his PhD and Master’s degrees in computer science from Hunan University, China, in 2010 and 2006, respectively, and earned his BS degree in circuits and systems from Hunan Normal University, China in 1996. Since 2011, he has been working as a professor in the College of Computer Science and Technology at the Hengyang Normal University, China. His research interests include embedded computing and information security.
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Ou, Y., Li, L. Side-channel analysis attacks based on deep learning network. Front. Comput. Sci. 16, 162303 (2022). https://doi.org/10.1007/s11704-020-0209-4
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DOI: https://doi.org/10.1007/s11704-020-0209-4