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Efficient Non-profiled Side Channel Attack Using Multi-output Classification Neural Network

Hoang, V. and Do, N. and Doan, V.S. (2022) Efficient Non-profiled Side Channel Attack Using Multi-output Classification Neural Network. IEEE Embedded Systems Letters. p. 1. ISSN 19430663

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Abstract

Differential Deep Learning Analysis (DDLA) is the first deep learning based non-profiled side-channel attack (SCA) on embedded systems. However, DDLA requires many training processes to distinguish the correct key. In this letter, we introduce a non-profiled SCA technique using multi-output classification to mitigate the aforementioned issue. Specifically, a multi-output multi-layer perceptron and a multi-output convolutional neural network are introduced against various SCA protected schemes, such as masking, noise generation, and trace de-synchronization countermeasures. The experimental results on different power side channel datasets have clarified that our model performs the attack up to 9 and 30 times faster than DDLA in the case of masking and de-synchronization countermeasures, respectively. In addition, regarding combined masking and noise generation countermeasure, our proposed model achieves a higher success rate of at least 20 in the cases of the standard deviation equal to 1.0 and 1.5. IEEE

Item Type: Article
Divisions: Institutes > Institute of System Integration
Identification Number: 10.1109/LES.2022.3213443
Uncontrolled Keywords: Computer architecture; deep learning; Deep learning; Embedded systems; embedded systems; multi-loss; multi-output; Neural networks; Proposals; Side channel attacks; Side-channel attacks; Training
URI: http://eprints.lqdtu.edu.vn/id/eprint/10575

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