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

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 classifi...

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Bibliographic Details
Published in:IEEE embedded systems letters 2022, p.1-1
Main Authors: Hoang, Van-Phuc, Do, Ngoc-Tuan, Doan, Van Sang
Format: Article
Language:English
Subjects:
Computer architecture
Deep learning
Embedded systems
multi-loss
multi-output
Neural networks
Proposals
Side channel attacks
Training
Online Access:Get full text
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Summary: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.
ISSN:1943-0663
DOI:10.1109/LES.2022.3213443