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Lookup NU author(s): Dr Farhad Merchant
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Logic locking has emerged as a prominent key-driven technique to protect the integrity of integrated circuits. However, novel machine-learning-based attacks have recently been introduced to challenge the security foundations of locking schemes. These attacks are able to recover a significant percentage of the key without having access to an activated circuit. This article address this issue through two focal points. First, we present a theoretical model to test locking schemes for key-related structural leakage that can be exploited by machine learning. Second, based on the theoretical model, we introduce D-MUX: a deceptive multiplexer-based logic-locking scheme that is resilient against structure-exploiting machine learning attacks. Through the design of D-MUX, we uncover a major fallacy in the existing multiplexer-based locking schemes in the form of a structural-analysis attack. Finally, an extensive cost evaluation of D-MUX is presented. To the best of our knowledge, D-MUX is the first machine-learning-resilient locking scheme capable of protecting against all known learning-based attacks. Hereby, the presented work offers a starting point for the design and evaluation of future-generation logic locking in the era of machine learning.
Author(s): Sisejkovic D, Merchant F, Reimann L, Leupers R
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Year: 2022
Volume: 41
Issue: 6
Pages: 1716-1729
Print publication date: 01/06/2022
Online publication date: 26/07/2021
Acceptance date: 18/07/2021
ISSN (print): 0278-0070
ISSN (electronic): 1937-4151
Publisher: IEEE
URL: https://doi.org/10.1109/TCAD.2021.3100275
DOI: 10.1109/TCAD.2021.3100275
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