Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities

Dominik Sisejkovic; Lennart M. Reimann; Elmira Moussavi; Farhad Merchant; Rainer Leupers

doi:10.1109/VLSI-SoC53125.2021.9606979

Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities

Dominik Sisejkovic, Lennart M. Reimann, Elmira Moussavi, Farhad Merchant, Rainer Leupers

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

14 Citations (Scopus)

Abstract

In the past decade, a lot of progress has been made in the design and evaluation of logic locking; a premier technique to safeguard the integrity of integrated circuits throughout the electronics supply chain. However, the widespread proliferation of machine learning has recently introduced a new pathway to evaluating logic locking schemes. This paper summarizes the recent developments in logic locking attacks and countermeasures at the frontiers of contemporary machine learning models. Based on the presented work, the key takeaways, opportunities, and challenges are highlighted to offer recommendations for the design of next-generation logic locking.

Original language	English
Title of host publication	2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC)
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Print)	978-1-6654-2615-2
DOIs	https://doi.org/10.1109/VLSI-SoC53125.2021.9606979
Publication status	Published - 7-Oct-2021
Externally published	Yes
Event	2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC) - Singapore, Singapore Duration: 4-Oct-2021 → 7-Oct-2021

Conference

Conference	2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC)
Period	04/10/2021 → 07/10/2021

Keywords

Supply chains
Machine learning
Very large scale integration
Security
Integrated circuit modeling
Electronic countermeasures
Next generation networking

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10.1109/VLSI-SoC53125.2021.9606979

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@inproceedings{3f65fa29b9184672bd761873f21be312,

title = "Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities",

abstract = "In the past decade, a lot of progress has been made in the design and evaluation of logic locking; a premier technique to safeguard the integrity of integrated circuits throughout the electronics supply chain. However, the widespread proliferation of machine learning has recently introduced a new pathway to evaluating logic locking schemes. This paper summarizes the recent developments in logic locking attacks and countermeasures at the frontiers of contemporary machine learning models. Based on the presented work, the key takeaways, opportunities, and challenges are highlighted to offer recommendations for the design of next-generation logic locking.",

keywords = "Supply chains, Machine learning, Very large scale integration, Security, Integrated circuit modeling, Electronic countermeasures, Next generation networking",

author = "Dominik Sisejkovic and Reimann, {Lennart M.} and Elmira Moussavi and Farhad Merchant and Rainer Leupers",

year = "2021",

month = oct,

day = "7",

doi = "10.1109/VLSI-SoC53125.2021.9606979",

language = "English",

isbn = "978-1-6654-2615-2",

pages = "1--6",

booktitle = "2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC)",

publisher = "IEEE",

note = "2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC) ; Conference date: 04-10-2021 Through 07-10-2021",

}

Sisejkovic, D, Reimann, LM, Moussavi, E, Merchant, F & Leupers, R 2021, Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities. in 2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC)., 9606979, IEEE, pp. 1-6, 2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC), 04/10/2021. https://doi.org/10.1109/VLSI-SoC53125.2021.9606979

Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities. / Sisejkovic, Dominik; Reimann, Lennart M.; Moussavi, Elmira et al.
2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC). IEEE, 2021. p. 1-6 9606979.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Logic Locking at the Frontiers of Machine Learning

T2 - 2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC)

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AU - Reimann, Lennart M.

AU - Moussavi, Elmira

AU - Merchant, Farhad

AU - Leupers, Rainer

PY - 2021/10/7

Y1 - 2021/10/7

N2 - In the past decade, a lot of progress has been made in the design and evaluation of logic locking; a premier technique to safeguard the integrity of integrated circuits throughout the electronics supply chain. However, the widespread proliferation of machine learning has recently introduced a new pathway to evaluating logic locking schemes. This paper summarizes the recent developments in logic locking attacks and countermeasures at the frontiers of contemporary machine learning models. Based on the presented work, the key takeaways, opportunities, and challenges are highlighted to offer recommendations for the design of next-generation logic locking.

AB - In the past decade, a lot of progress has been made in the design and evaluation of logic locking; a premier technique to safeguard the integrity of integrated circuits throughout the electronics supply chain. However, the widespread proliferation of machine learning has recently introduced a new pathway to evaluating logic locking schemes. This paper summarizes the recent developments in logic locking attacks and countermeasures at the frontiers of contemporary machine learning models. Based on the presented work, the key takeaways, opportunities, and challenges are highlighted to offer recommendations for the design of next-generation logic locking.

KW - Supply chains

KW - Machine learning

KW - Very large scale integration

KW - Security

KW - Integrated circuit modeling

KW - Electronic countermeasures

KW - Next generation networking

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DO - 10.1109/VLSI-SoC53125.2021.9606979

M3 - Conference contribution

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BT - 2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC)

PB - IEEE

Y2 - 4 October 2021 through 7 October 2021

ER -

Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities

Abstract

Conference

Keywords

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