research-article

Neural Network-based Inherently Fault-tolerant Hardware Cryptographic Primitives without Explicit Redundancy Checks

Authors:

Debapriya Basu Roy,

Debdeep MukhopadhyayAuthors Info & Claims

ACM Journal on Emerging Technologies in Computing Systems (JETC), Volume 17, Issue 1

Article No.: 3, Pages 1 - 30

https://doi.org/10.1145/3409594

Published: 22 September 2020 Publication History

Abstract

Fault injection-based cryptanalysis is one of the most powerful practical threats to modern cryptographic primitives. Popular countermeasures to such fault-based attacks generally use some form of redundant computation to detect and react/correct the injected faults. However, such countermeasures are shown to be vulnerable to selective fault injections. In this article, we aim to develop a cryptographic primitive that is fault tolerant by its construction and does not require to compute the same value multiple times. We utilize the effectiveness of Neural Networks (NNs), which show “some degree” of robustness by functioning correctly even after the occurrence of faults in any of its parameters. We also propose a novel strategy that enhances the fault tolerance of the implementation to “high degree” (close to 100%) by incorporating selective constraints in the NN parameters during the training phase. We evaluated the performance of revised NN considering both software and FPGA implementations for standard cryptographic primitives like 8×8 AES SBox and 4×4 PRESENT SBox. The results show that the fault tolerance of such implementations can be significantly increased with the proposed methodology. Such NN-based cryptographic primitives will provide inherent resistance against fault injections without requiring any redundancy countermeasures.

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Cited By

Xie JZhao WLee HRoy DZhang X(2024)Hardware Circuits and Systems Design for Post-Quantum Cryptography—A Tutorial BriefIEEE Transactions on Circuits and Systems II: Express Briefs10.1109/TCSII.2024.335783671:3(1670-1676)Online publication date: Mar-2024
https://doi.org/10.1109/TCSII.2024.3357836
Rashidi B(2023)Error‐correcting cryptographic S‐boxes with multiple error detection and correctionInternational Journal of Circuit Theory and Applications10.1002/cta.3703Online publication date: 11-Jun-2023
https://doi.org/10.1002/cta.3703
Jap DWon YBhasin SPalesi MTumeo AGoumas GAlmudever C(2021)Fault injection attacks on SoftMax function in deep neural networksProceedings of the 18th ACM International Conference on Computing Frontiers10.1145/3457388.3458870(238-240)Online publication date: 11-May-2021
https://dl.acm.org/doi/10.1145/3457388.3458870

Index Terms

Neural Network-based Inherently Fault-tolerant Hardware Cryptographic Primitives without Explicit Redundancy Checks

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Published In

cover image ACM Journal on Emerging Technologies in Computing Systems

ACM Journal on Emerging Technologies in Computing Systems Volume 17, Issue 1

January 2021

232 pages

ISSN:1550-4832

EISSN:1550-4840

DOI:10.1145/3425108

Editor:
Ramesh Karri
Polytechnic Institute of New York University, USA

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Copyright © 2020 ACM.

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 22 September 2020

Accepted: 01 July 2020

Revised: 01 June 2020

Received: 01 January 2020

Published in JETC Volume 17, Issue 1

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DeitY, Government of India (Information Security Education Awareness (ISEA)
Singapore National Research Foundation “SOCure”

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Cited By

Xie JZhao WLee HRoy DZhang X(2024)Hardware Circuits and Systems Design for Post-Quantum Cryptography—A Tutorial BriefIEEE Transactions on Circuits and Systems II: Express Briefs10.1109/TCSII.2024.335783671:3(1670-1676)Online publication date: Mar-2024
https://doi.org/10.1109/TCSII.2024.3357836
Rashidi B(2023)Error‐correcting cryptographic S‐boxes with multiple error detection and correctionInternational Journal of Circuit Theory and Applications10.1002/cta.3703Online publication date: 11-Jun-2023
https://doi.org/10.1002/cta.3703
Jap DWon YBhasin SPalesi MTumeo AGoumas GAlmudever C(2021)Fault injection attacks on SoftMax function in deep neural networksProceedings of the 18th ACM International Conference on Computing Frontiers10.1145/3457388.3458870(238-240)Online publication date: 11-May-2021
https://dl.acm.org/doi/10.1145/3457388.3458870

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