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.

翻译：逻辑锁定（LL）作为一种有前景的集成电路知识产权保护措施已受到广泛关注。然而，近期基于机器学习（ML）的攻击通过利用正确密钥值与电路结构的相关性，展示了在多种逻辑锁定方案中预测正确密钥的潜在能力。本文提出了一种基于随机算法的通用逻辑锁定增强方法，该方法能够显著降低逻辑锁定方案中锁定电路网表与正确密钥值之间的相关性。数值结果表明，所提方法可将最先进的基于机器学习的攻击精度有效降低至约50%，使其相比随机猜测几乎无优势。