Side-channel analysis attacks based on deep learning network

doi:10.1007/s11704-020-0209-4

Front. Comput. Sci.

2022, Vol. 16

Issue (2) : 162303 https://doi.org/10.1007/s11704-020-0209-4

RESEARCH ARTICLE

Side-channel analysis attacks based on deep learning network

Yu OU^1,², Lang LI^1,^2,³(

)

¹. Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang 421002, China
². College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China
³. College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China

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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.

Keywords side-channel analysis template attack machine learning deep learning

Corresponding Author(s): Lang LI

Just Accepted Date: 30 July 2020 Issue Date: 08 September 2021

Cite this article:

Yu OU,Lang LI. Side-channel analysis attacks based on deep learning network[J]. Front. Comput. Sci., 2022, 16(2): 162303.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-020-0209-4
https://academic.hep.com.cn/fcs/EN/Y2022/V16/I2/162303

Fig.1 MLP in template analysis

Fig.2 Single neural node

Fig.3 Convolution layer and pooling layer

Fig.4 TAPDC model

Fig.5 700-dim power trace

Fig.6 Autoencoder structure

Fig.7

Fig.8 The result or pre-training

Fig.9 Training result of MLP with (a) Training-set loss; (b) Testing-set loss

Fig.10 Attack results of MLP

Fig.11 Training result of CNN with (a) Training-set loss; (b) Testing-set loss

Fig.12 Attack result of CNN

Fig.13 Training result of TAPDC with (a) Training-set loss; (b) Testing-set loss

Fig.14 Attack result of TAPDC

Tab.1 Result of Accuracy

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