skip to main content
10.1145/3603273.3630505acmotherconferencesArticle/Chapter ViewAbstractPublication PagesaaiaConference Proceedingsconference-collections
research-article

Side-Channel Attacks Based on Deep Learning

Published: 09 January 2024 Publication History

Abstract

The technological and economic competition between countries has resulted in the frantic development of industrial technology and financial technology. In recent years, many Internet-centric smart terminal devices have basically covered all aspects of life. However, people's privacy, corporate transaction documents, and state secrets are all threatened by intelligence. Its threat not only comes from the communication threat in the network, but the most hidden danger of the threat comes from the "disclosure" of side information in the cryptographic security chip. Through the template attack principle and strategy that has been proposed, aiming at the problem of low attack success rate in the template attack, PCA dimensionality reduction is performed on the data set extracted by CWLITE. Then use feature normalization processing and correlation analysis to extract the output data of the first round of AES S-boxes in the data set, input them into the built convolutional neural network and CNN-BILSTM network, and give Comparative Results.

References

[1]
Wang R, Wang H, Dubroval E. 2020 Far Field EM Side-channel Attack on AES Using Deep Learning. Proceedings of the 4th ACM Workshop on Attacks and Solutions in Hardware Security, Orlando, USA, pp. 35-44.
[2]
Cagli E, Dumas C, Prouff E. 2017 Convolutional Neural Networks with Data Augmentation Against Jitter-based Countermeasures: Profiling Attacks without Pre-processing. Cryptographic Hardware and Embedded Systems–CHES 2017: 19th International Conference, Taiwan, China, pp. 45-68.
[3]
Wu L, Perin G, Picek S. 2022 I choose you: Automated hyperparameter tuning for deep learning based side-channel analysis. IEEE Transactions on Emerging Topics in Computing.
[4]
Moos T, Wegener F, Moradi A 2021 DL-LA: Deep learning leakage assessment: A modern roadmap for SCA evaluations. IACR Transactions on Cryptographic Hardware and Embedded Systems: 552-598.
[5]
Picek S, Heuser a, Jovic A, 2019 The curse of class imbalance and conflicting metrics with machine learning for side-channel evaluations. IACR Transactions on Cryptographic Hardware and Embedded Systems 2019(1): 1-29.
[6]
Gardner M W, Dorling S R 1998 Artificial neural networks (the multilayer perceptron)—A review of applications in the atmospheric sciences. Atmospheric Environment 32(14-15): 2627-2636.
[7]
Gu J, Wang Z, Kuen J, 2018 Recent advances in convolutional neural networks. Pattern Recognition 77: 354-377.

Cited By

View all
  • (2024)Review on Hybrid Deep Learning Models for Enhancing Encryption Techniques Against Side Channel AttacksIEEE Access10.1109/ACCESS.2024.343121812(188435-188453)Online publication date: 2024

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
AAIA '23: Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications
November 2023
406 pages
ISBN:9798400708268
DOI:10.1145/3603273
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 January 2024

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. AES Encryption
  2. Side-channel attack
  3. power consumption attack
  4. template attack

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

AAIA 2023

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)55
  • Downloads (Last 6 weeks)3
Reflects downloads up to 17 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Review on Hybrid Deep Learning Models for Enhancing Encryption Techniques Against Side Channel AttacksIEEE Access10.1109/ACCESS.2024.343121812(188435-188453)Online publication date: 2024

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media