research-article

AL-PA: cross-device profiled side-channel attack using adversarial learning

Authors:

Yidong YuanAuthors Info & Claims

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

Pages 691 - 696

https://doi.org/10.1145/3489517.3530517

Published: 23 August 2022 Publication History

Abstract

In this paper, we focus on the portability issue in profiled side-channel attacks (SCAs) that arises due to significant device-to-device variations. Device discrepancy is inevitable in realistic attacks, but it is often neglected in research works. In this paper, we identify such device variations and take a further step towards leveraging the transferability of neural networks. We propose a novel adversarial learning-based profiled attack (AL-PA), which enables our neural network to learn device-invariant features. We evaluated our strategy on eight XMEGA microcontrollers. Without the need for target-specific preprocessing and multiple profiling devices, our approach has outperformed the state-of-the-art methods.

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Cited By

Lu ZLongde YFei WAidong CNing YXiang LJiancheng ZYanlong ZShuo WJing Z(2025)SCARefusion: Side channel analysis data restoration with diffusion modelMicroelectronics Journal10.1016/j.mejo.2024.106546156(106546)Online publication date: Feb-2025
https://doi.org/10.1016/j.mejo.2024.106546
He DWang HDeng TLiu JWang J(2025)Improving IIoT security: Unveiling threats through advanced side-channel analysisComputers & Security10.1016/j.cose.2024.104135148(104135)Online publication date: Jan-2025
https://doi.org/10.1016/j.cose.2024.104135
Xiao CTang M(2024)Acnn: arbitrary trace attacks based on leakage area detectionInternational Journal of Information Security10.1007/s10207-024-00874-423:4(2991-3006)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s10207-024-00874-4
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cover image ACM Conferences

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

July 2022

1462 pages

ISBN:9781450391429

DOI:10.1145/3489517

General Chair:
Rob Oshana
NXP

Copyright © 2022 ACM.

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SIGDA: ACM Special Interest Group on Design Automation
IEEE CEDA

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New York, NY, United States

Publication History

Published: 23 August 2022

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Funding Sources

National Natural Science Foundation of China

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DAC '22

Sponsor:

SIGDA

DAC '22: 59th ACM/IEEE Design Automation Conference

July 10 - 14, 2022

California, San Francisco

Acceptance Rates

Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

Upcoming Conference

DAC '25

Sponsor:
sigda

62nd ACM/IEEE Design Automation Conference

June 22 - 26, 2025

San Francisco , CA , USA

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Cited By

Lu ZLongde YFei WAidong CNing YXiang LJiancheng ZYanlong ZShuo WJing Z(2025)SCARefusion: Side channel analysis data restoration with diffusion modelMicroelectronics Journal10.1016/j.mejo.2024.106546156(106546)Online publication date: Feb-2025
https://doi.org/10.1016/j.mejo.2024.106546
He DWang HDeng TLiu JWang J(2025)Improving IIoT security: Unveiling threats through advanced side-channel analysisComputers & Security10.1016/j.cose.2024.104135148(104135)Online publication date: Jan-2025
https://doi.org/10.1016/j.cose.2024.104135
Xiao CTang M(2024)Acnn: arbitrary trace attacks based on leakage area detectionInternational Journal of Information Security10.1007/s10207-024-00874-423:4(2991-3006)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s10207-024-00874-4
Karayalçın SKrček MWu LPicek SPerin G(2024)It’s a Kind of Magic: A Novel Conditional GAN Framework for Efficient Profiling Side-Channel AnalysisAdvances in Cryptology – ASIACRYPT 202410.1007/978-981-96-0944-4_4(99-131)Online publication date: 10-Dec-2024
https://dl.acm.org/doi/10.1007/978-981-96-0944-4_4
Li XYang NLiu WChen AZhang YWang SZhou J(2024)Domain‐Adaptive Power Profiling Analysis Strategy for the MetaverseInternational Journal of Network Management10.1002/nem.228835:1Online publication date: 10-Jul-2024
https://doi.org/10.1002/nem.2288
Yu HWang MSong XShan HQiu HWang JYang K(2023)Noise2Clean: Cross-Device Side-Channel Traces Denoising with Unsupervised Deep LearningElectronics10.3390/electronics1204105412:4(1054)Online publication date: 20-Feb-2023
https://doi.org/10.3390/electronics12041054
Yu HWang SShan HPanoff MLee MYang KJin Y(2023)Dual-Leak: Deep Unsupervised Active Learning for Cross-Device Profiled Side-Channel Leakage Analysis2023 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)10.1109/HOST55118.2023.10133491(144-154)Online publication date: 1-May-2023
https://doi.org/10.1109/HOST55118.2023.10133491
Krček MPerin G(2023)Autoencoder-enabled model portability for reducing hyperparameter tuning efforts in side-channel analysisJournal of Cryptographic Engineering10.1007/s13389-023-00330-414:3(475-497)Online publication date: 21-Jul-2023
https://doi.org/10.1007/s13389-023-00330-4

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