DECOR: Enhancing Logic Locking Against Machine Learning-Based Attacks

• URL: http://arxiv.org/abs/2403.01789v1
• Date: Mon, 4 Mar 2024 07:31:23 GMT
• Title: DECOR: Enhancing Logic Locking Against Machine Learning-Based Attacks
• Authors: Yinghua Hu, Kaixin Yang, Subhajit Dutta Chowdhury, Pierluigi Nuzzo,
• Abstract summary: Logic locking (LL) has gained attention as a promising intellectual property protection measure for integrated circuits. Recent attacks, facilitated by machine learning (ML), have shown the potential to predict the correct key in multiple LL schemes. This paper presents a generic LL enhancement method based on a randomized algorithm that can significantly decrease the correlation between locked circuit netlist and correct key values in an LL scheme.
• Score: 0.6131022957085439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Logic locking (LL) has gained attention as a promising intellectual property protection measure for integrated circuits. However, recent attacks, facilitated by machine learning (ML), have shown the potential to predict the correct key in multiple LL schemes by exploiting the correlation of the correct key value with the circuit structure. This paper presents a generic LL enhancement method based on a randomized algorithm that can significantly decrease the correlation between locked circuit netlist and correct key values in an LL scheme. Numerical results show that the proposed method can efficiently degrade the accuracy of state-of-the-art ML-based attacks down to around 50%, resulting in negligible advantage versus random guessing.

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