Deceptive Logic Locking for Hardware Integrity Protection against Machine Learning Attacks

• URL: http://arxiv.org/abs/2107.08695v1
• Date: Mon, 19 Jul 2021 09:08:14 GMT
• Title: Deceptive Logic Locking for Hardware Integrity Protection against Machine Learning Attacks
• Authors: Dominik Sisejkovic, Farhad Merchant, Lennart M. Reimann, Rainer Leupers
• Abstract summary: We present a theoretical model to test locking schemes for key-related structural leakage that can be exploited by machine learning. We introduce D-MUX: a deceptive multiplexer-based logic-locking scheme that is resilient against structure-exploiting machine learning attacks. 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.
• Score: 0.6868387710209244
• License: http://creativecommons.org/licenses/by/4.0/
• 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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