- Authors:
-
Šišejković,
D.
,
Merchant,
F.
,
Reimann,
L. M.
,
Leupers,
R.
- Journal:
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD)
- Date:
-
Jul. 2021
- ISSN:
- 1937-4151
- DOI:
- 10.1109/TCAD.2021.3100275
- hsb:
- RWTH-2021-11818
- Language:
- English
Abstract
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 paper 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 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.
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