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Automated Side-Channel Attacks using Black-Box Neural Architecture Search

Published: 29 August 2023 Publication History

Abstract

The application of convolutional neural networks (CNNs) to break cryptographic systems through hardware side-channels facilitated rapid and adaptable attacks on cryptographic systems like smart cards and Trusted Platform Modules (TPMs). However, current approaches rely on manually designed CNN architectures by domain experts, which are time-consuming and impractical for attacking new systems.
To overcome this, recent research has delved into the use of neural architecture search (NAS) to discover appropriate CNN architectures automatically. This approach aims to alleviate the burden on human experts and facilitate more efficient exploration of new attack targets. However, these works only optimize the architecture using the secret key information from the attack dataset and explore limited search strategies with one-dimensional CNNs. In this work, we propose a fully black-box NAS approach that solely utilizes the profiling dataset for optimization. Through an extensive experimental parameter study, we investigate which choices for NAS, such as using 1-D or 2-D CNNs and various search strategies, produce the best results on 10 state-of-the-art datasets for identity leakage model.
Our results demonstrate that applying the Random search strategy on 1-D inputs achieves a high success rate, enabling retrieval of the correct secret key using a single attack trace on two datasets. This combination matches the attack efficiency of fixed CNN architectures and outperforms them in 4 out of 10 datasets. Our experiments also emphasize the importance of repeated attack evaluations for ML-based solutions to avoid biased performance estimates.

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Cited By

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  • (2024)Side-Channel Attacks Based on Multi-Loss Regularized Denoising AutoEncoderIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.334394719(2051-2065)Online publication date: 1-Jan-2024
  • (2024)Secure AI for 6G Mobile Devices: Deep Learning Optimization Against Side-Channel AttacksIEEE Transactions on Consumer Electronics10.1109/TCE.2024.337201870:1(3951-3959)Online publication date: Feb-2024
  • (2024)Cover to Uncover: Comprehensive Study of Occlusion in DL-based SCA2024 IEEE Physical Assurance and Inspection of Electronics (PAINE)10.1109/PAINE62042.2024.10792754(1-7)Online publication date: 12-Nov-2024
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cover image ACM Other conferences
ARES '23: Proceedings of the 18th International Conference on Availability, Reliability and Security
August 2023
1440 pages
ISBN:9798400707728
DOI:10.1145/3600160
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].

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Publication History

Published: 29 August 2023

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Author Tags

  1. AES
  2. Convolutional Neural Network
  3. Neural Architecture Search
  4. Parameter Study
  5. Side-Channel Attack

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  • Research-article
  • Research
  • Refereed limited

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  • Bundesministerium für Bildung und Forschung (BMBF)

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ARES 2023

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Overall Acceptance Rate 228 of 451 submissions, 51%

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Cited By

View all
  • (2024)Side-Channel Attacks Based on Multi-Loss Regularized Denoising AutoEncoderIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.334394719(2051-2065)Online publication date: 1-Jan-2024
  • (2024)Secure AI for 6G Mobile Devices: Deep Learning Optimization Against Side-Channel AttacksIEEE Transactions on Consumer Electronics10.1109/TCE.2024.337201870:1(3951-3959)Online publication date: Feb-2024
  • (2024)Cover to Uncover: Comprehensive Study of Occlusion in DL-based SCA2024 IEEE Physical Assurance and Inspection of Electronics (PAINE)10.1109/PAINE62042.2024.10792754(1-7)Online publication date: 12-Nov-2024
  • (2024)AI-Enabled Hardware SecurityAI-Enabled Electronic Circuit and System Design10.1007/978-3-031-71436-8_9(309-342)Online publication date: 17-Oct-2024

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