Side Channel Attacks Based on Densely Connected Convolutional Networks with Attention Mechanism-Reference-Cited by-同舟云学术

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Side Channel Attacks Based on Densely Connected Convolutional Networks with Attention Mechanism

Published:2024-05-10 Issue: Volume:2023 Page:229-234
ISSN:
Container-title:Proceedings of the 2024 International Conference on Generative Artificial Intelligence and Information Security
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Author:

Zhang Runlian¹^ORCID,Hou Minghui¹^ORCID,Cheng Wentao¹^ORCID,Wu Xiaonian¹^ORCID

Affiliation:

1. Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin, China

Funder

the National Natural Science Foundation of China

the Key Research and Development Program of Guangxi in China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3665348.3665388

Reference14 articles.

1. A novel non-profiled side channel attack based on multi-output regression neural network[J];N T;Journal of Cryptographic Engineering

2. A.-T. Hoang, N. Hanley, and M. O'Neill, "Plaintext: A Missing Feature for Enhancing the Power of Deep Learning in Side-Channel Analysis? Breaking multiple layers of side-channel countermeasures", TCHES, vol. 2020, no. 4, pp. 49–85, Aug. 2020.

3. Side-channel Attacks Based on CBAPD Network;Dong Z.;Journal of Cryptologic Research,2022

4. Resolving the Doubts: On the Construction and Use of ResNets for Side-Channel Analysis

5. To overfit, or not to overfit: improving the performance of deep learning-based SCA;Rezaeezade A.;International Conference on Cryptology in Africa,2022

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