Research on electromagnetic attack of advanced encryption standard based on long short-term memory and sparse autoencoder

Bo Gao; Lin Chen; Yingjian Yan

doi:10.1117/12.2653520

8 December 2022 Research on electromagnetic attack of advanced encryption standard based on long short-term memory and sparse autoencoder

Bo Gao, Lin Chen, Yingjian Yan

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Proceedings Volume 12474, Second International Symposium on Computer Technology and Information Science (ISCTIS 2022); 1247403 (2022) https://doi.org/10.1117/12.2653520
Event: Second International Symposium on Computer Technology and Information Science (ISCTIS 2022), 2022, Guilin, China

Abstract

Deep learning techniques have been widely used in the field of Side Channel Attack (SCA), which poses a serious threat to the security of cryptographic algorithms. However, deep learning-based side channel attack also has problems such as inefficient models, poor robustness, and longtime consumption. To address these problems, this paper focuses on the performance of Long Short-term Memory(LSTM) combining with the dimensional compression technique of Sparse Auto Encoder (SAE), and validates it on fully synchronized and unsynchronized EM traces captured under first-order bool mask protection. The experimental results show that compared with multilayer perceptron (MLP) and convolutional neural network (CNN), LSTM achieves more than 90% training accuracy and test accuracy, with higher robustness, lower parameters and faster convergence speed, even when the jitter in the dataset increases from 0 to 50 and 100.

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Bo Gao, Lin Chen, and Yingjian Yan "Research on electromagnetic attack of advanced encryption standard based on long short-term memory and sparse autoencoder", Proc. SPIE 12474, Second International Symposium on Computer Technology and Information Science (ISCTIS 2022), 1247403 (8 December 2022); https://doi.org/10.1117/12.2653520

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KEYWORDS

Data modeling

Electromagnetism

Performance modeling

Computer programming

Profiling

Databases

Neurons

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