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Machine learning and hardware security: challenges and opportunities

ICCAD '20: Proceedings of the 39th International Conference on Computer-Aided Design

Article No.: 141, Pages 1 - 6

https://doi.org/10.1145/3400302.3416260

Published: 17 December 2020 Publication History

Abstract

Machine learning techniques have significantly changed our lives. They helped improving our everyday routines, but they also demonstrated to be an extremely helpful tool for more advanced and complex applications. However, the implications of hardware security problems under a massive diffusion of machine learning techniques are still to be completely understood. This paper first highlights novel applications of machine learning for hardware security, such as evaluation of post quantum cryptography hardware and extraction of physically unclonable functions from neural networks. Later, practical model extraction attack based on electromagnetic side-channel measurements are demonstrated followed by a discussion of strategies to protect proprietary models by watermarking them.

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Cited By

Horváth PLauret DLiu ZBatina LBalzarotti DXu W(2024)SoKProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699091(3403-3422)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3699091
Hasan MHoque TGanji FWoodard DForte DShomaji S(2024)A Resource-Efficient Binary CNN Implementation for Enabling Contactless IoT AuthenticationJournal of Hardware and Systems Security10.1007/s41635-024-00153-78:3(160-173)Online publication date: 26-Jul-2024
https://doi.org/10.1007/s41635-024-00153-7
Nyako KDevkota SLi FBorra V(2023)Building Trust in Microelectronics: A Comprehensive Review of Current Techniques and Adoption ChallengesElectronics10.3390/electronics1222461812:22(4618)Online publication date: 11-Nov-2023
https://doi.org/10.3390/electronics12224618
Show More Cited By

Index Terms

Machine learning and hardware security: challenges and opportunities
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Security and privacy
  1. Security in hardware
    1. Hardware attacks and countermeasures
      1. Side-channel analysis and countermeasures

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cover image ACM Conferences

ICCAD '20: Proceedings of the 39th International Conference on Computer-Aided Design

November 2020

1396 pages

ISBN:9781450380263

DOI:10.1145/3400302

General Chair:
Yuan Xie
Univ. of California, Santa Barbara, CA

Copyright © 2020 ACM.

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 ACM 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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Published: 17 December 2020

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European Union
NSF
JST CREST, Japan

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ICCAD '20

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ICCAD '20: IEEE/ACM International Conference on Computer-Aided Design

November 2 - 5, 2020

Virtual Event, USA

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Overall Acceptance Rate 457 of 1,762 submissions, 26%

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Cited By

Horváth PLauret DLiu ZBatina LBalzarotti DXu W(2024)SoKProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699091(3403-3422)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3699091
Hasan MHoque TGanji FWoodard DForte DShomaji S(2024)A Resource-Efficient Binary CNN Implementation for Enabling Contactless IoT AuthenticationJournal of Hardware and Systems Security10.1007/s41635-024-00153-78:3(160-173)Online publication date: 26-Jul-2024
https://doi.org/10.1007/s41635-024-00153-7
Nyako KDevkota SLi FBorra V(2023)Building Trust in Microelectronics: A Comprehensive Review of Current Techniques and Adoption ChallengesElectronics10.3390/electronics1222461812:22(4618)Online publication date: 11-Nov-2023
https://doi.org/10.3390/electronics12224618
Chen HBabar M(2023)Security for Machine Learning-based Software Systems: a survey of threats, practices and challengesACM Computing Surveys10.1145/3638531Online publication date: 28-Dec-2023
https://doi.org/10.1145/3638531
Oliynyk DMayer RRauber A(2023)I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and DefencesACM Computing Surveys10.1145/359529255:14s(1-41)Online publication date: 29-Apr-2023
https://dl.acm.org/doi/10.1145/3595292
Vennila ABalambigai SArulmurugan AHema MKamali MKishore M(2023)XG Boost Algorithm based Hardware Trojan Detection in Hardware Circuits2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)10.1109/ICECCT56650.2023.10179698(1-5)Online publication date: 22-Feb-2023
https://doi.org/10.1109/ICECCT56650.2023.10179698
Amuru DZahra AVudumula HCherupally PGurram SAhmad AAbbas Z(2023)AI/ML algorithms and applications in VLSI design and technologyIntegration10.1016/j.vlsi.2023.06.00293(102048)Online publication date: Nov-2023
https://doi.org/10.1016/j.vlsi.2023.06.002
Farahmandi FRahman MRajendran STehranipoor MFarahmandi FRahman MRajendran STehranipoor M(2023)The Future of CAD for Hardware SecurityCAD for Hardware Security10.1007/978-3-031-26896-0_18(397-403)Online publication date: 28-Jan-2023
https://doi.org/10.1007/978-3-031-26896-0_18
Farahmandi FRahman MRajendran STehranipoor MFarahmandi FRahman MRajendran STehranipoor M(2023)CAD for Machine Learning in Hardware SecurityCAD for Hardware Security10.1007/978-3-031-26896-0_10(211-230)Online publication date: 28-Jan-2023
https://doi.org/10.1007/978-3-031-26896-0_10
Takatoi GSugawara TSakiyama KHara-Azumi YLi Y(2022)The Limits of SEMA on Distinguishing Similar Activation Functions of Embedded Deep Neural NetworksApplied Sciences10.3390/app1209413512:9(4135)Online publication date: 20-Apr-2022
https://doi.org/10.3390/app12094135
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