A Side Channel Attack Based on Multi-Source Data Aggregation Neural Network

Zhang Runlian; Pan Zhaoxuan; Li Jinlin; Wu Xiaonian; Wei Yongzhuang

doi:10.7544/issn1000-1239.202220172

Journal of Computer Research and Development > 2024 > 61(1): 261-270. > DOI: 10.7544/issn1000-1239.202220172

Zhang Runlian, Pan Zhaoxuan, Li Jinlin, Wu Xiaonian, Wei Yongzhuang. A Side Channel Attack Based on Multi-Source Data Aggregation Neural Network[J]. Journal of Computer Research and Development, 2024, 61(1): 261-270. DOI: 10.7544/issn1000-1239.202220172

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A Side Channel Attack Based on Multi-Source Data Aggregation Neural Network

Key Laboratory of Cryptography and Information Security (Guilin University of Electronic Technology), Guilin, Guangxi 541004

Funds: This work was supported by the National Natural Science Foundation of China (62062026, 61872103), the Innovation Research Team Project of Guangxi (2019GXNSFGA245004), the Scientific Research Project of Young Innovative Talents of Guangxi (guike AD20238082), the Guangxi Natural Science Foundation (2020GXNSFBA297076), and the Graduate Innovation Project of Guangxi (2022YCXS082).

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Author Bio:
Zhang Runlian: born in 1974. PhD, associate professor. Her main research interests include information security and distributed computing

Pan Zhaoxuan: born in 1997. Master candidate. His main research interest includes side channel analysis

Li Jinlin: born in 1997. Master candidate. His main research interests include network security and application, and side channel analysis

Wu Xiaonian: born in 1972. Master, professor. His main research interests include information security and distributed computing

Wei Yongzhuang: born in 1976. PhD, professors, PhD supervisor. His main research interests include symmetric ciphers and security analysis of protocol
Received Date: February 28, 2022
Revised Date: December 22, 2022
Available Online: April 17, 2023

Graphical Abstract

Abstract

Abstract

Side channel attack based on deep learning needs to model and train each key byte of the cryptographic algorithm, which costs a lot of data acquisition and model training. To solve this problem, a side channel attack method based on multi-source data aggregation neural network is proposed. In order to screen the leaked data of key byte with good generalization quality for data aggregation, taking AES-128 algorithm as an example, firstly 16 single key byte models are trained based on the leaked data of 16 key bytes, and models are used to recover 16 key bytes respectively. Secondly, a scoring mechanism is designed to evaluate the generalization effect of each single key byte model, and models with the best recovery effect for each key byte are selected according to score sorting. Finally, a multi-source data aggregation model is constructed based on the key byte leaked data sets corresponding to the selected models to realize key recovery. The tested results show that the multi-source data aggregation model has good generalization effect, effectively improves the accuracy and efficiency of key recovery, reduces the number of traces used to recover the key, and the model also has good attack effect in the case of less traces.
- side channel attack,
- neural network,
- scoring mechanism,
- data aggregation,
- data labeling

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References (18)

References

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