Work

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

Year: 2021

Type: conference-paper

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.

Source: IFIP/IEEE International Conference on Very Large Scale Integration

DOI: 10.1109/vlsi-soc53125.2021.9606979

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

Institution: RWTH Aachen University

Language: English

Topic: Physical Unclonable Functions (PUFs) and Hardware Security

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3177600879

FWCI: 4.545

Cites: 48

Cited by: 30

Related to: 20