Performance Analysis of Deep Learning Based Non-profiled Side Channel Attacks Using Significant Hamming Weight Labeling
Author:
Funder
National Foundation for Science and Technology Development
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s11036-023-02128-4.pdf
Reference15 articles.
1. Hettwer TGB, Gehrer S (2020) Applications of machine learning techniques in side-channel attacks: a survey. J Cryptogr Eng 10:135–162
2. Timon B (2019) Non-Profiled Deep Learning-based Side-Channel attacks with Sensitivity Analysis. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2019(2), 107–131. https://doi.org/10.13154/tches.v2019.i2.107-131
3. Picek S, Samiotis IP, Kim J, Heuser A, Bhasin S, Legay A (2018) On the performance of convolutional neural networks for side-channel analysis. In: Chattopadhyay A, Rebeiro C, Yarom Y (eds) Security, Privacy, and Applied Cryptography Engineering. Springer International Publishing, Cham, pp 157–176
4. Alipour A, Papadimitriou A, Beroulle V, Aerabi E, Hély D (2020) On the performance of non-profiled differential deep learning attacks against an aes encryption algorithm protected using a correlated noise generation based hiding countermeasure. In: 2020 Design, Automation Test in Europe Conference Exhibition (DATE). pp 614–617
5. Won Y-S, Han D-G, Jap D, Bhasin S, Park J-Y (2021) Non-profiled side-channel attack based on deep learning using picture trace. IEEE Access 9:22 480–22 492
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