Logic locking aims to protect the intellectual property (IP) of integrated circuit (IC) designs throughout the globalized supply chain. The SAIL attack, based on tailored machine learning (ML) models, circumvents combinational logic locking with high accuracy and is amongst the most potent attacks as it does not require a functional IC acting as an oracle. In this work, we propose UNSAIL, a logic locking technique that inserts key-gate structures with the specific aim to confuse ML models like those used in SAIL. More specifically, UNSAIL serves to prevent attacks seeking to resolve the structural transformations of synthesis-induced obfuscation, which is an essential step for logic locking. Our approach is generic; it can protect any local structure of key-gates against such ML-based attacks in an oracle-less setting. We develop a reference implementation for the SAIL attack and launch it on both traditionally locked and UNSAIL-locked designs. For SAIL, two ML models have been proposed (which we implement accordingly), namely a change-prediction model and a reconstruction model; the change-prediction model is used to determine which key-gate structures to restore using the reconstruction model. Our study on benchmarks ranging from the ISCAS-85 and ITC-99 suites to the OpenRISC Reference Platform System-on-Chip (ORPSoC) confirms that UNSAIL degrades the accuracy of the change-prediction model and the reconstruction model by an average of 20.13 and 17 percentage points (pp), respectively. When the aforementioned models are combined, which is the most powerful scenario for SAIL, UNSAIL reduces the attack accuracy of SAIL by an average of 11pp. We further demonstrate that UNSAIL thwarts other oracle-less attacks, i.e., SWEEP and the redundancy attack, indicating the generic nature and strength of our approach. Detailed layout-level evaluations illustrate that UNSAIL incurs minimal area and power overheads of 0.26% and 0.61%, respectively, on the million-gate ORPSoC design.

中文翻译：

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UNSAIL：阻止针对逻辑锁定的无 Oracle 机器学习攻击


逻辑锁定旨在保护整个全球化供应链中集成电路（IC）设计的知识产权（IP）。 SAIL 攻击基于定制的机器学习 (ML) 模型，能够高精度地规避组合逻辑锁定，并且是最有效的攻击之一，因为它不需要充当预言机的功能 IC。在这项工作中，我们提出了 UNSAIL，这是一种插入键门结构的逻辑锁定技术，其具体目的是混淆 ML 模型，如 SAIL 中使用的模型。更具体地说，UNSAIL 用于防止寻求解决综合引起的混淆的结构转换的攻击，这是逻辑锁定的重要步骤。我们的方法是通用的；它可以在无预言机设置中保护密钥门的任何本地结构免受此类基于 ML 的攻击。我们为 SAIL 攻击开发了一个参考实现，并在传统锁定和 UNSAIL 锁定设计上启动它。对于 SAIL，已经提出了两个 ML 模型（我们相应地实现了），即变化预测模型和重建模型；变化预测模型用于确定使用重建模型恢复哪些键门结构。我们对从 ISCAS-85 和 ITC-99 套件到 OpenRISC 参考平台片上系统 (ORPSoC) 等基准的研究证实，UNSAIL 使变化预测模型和重建模型的准确性平均降低 20.13分别为 17 个百分点 (pp)。当上述模型组合起来时，这是 SAIL 最强大的场景，UNSAIL 使 SAIL 的攻击准确率平均降低了 11pp。我们进一步证明 UNSAIL 可以阻止其他无预言机攻击，即、SWEEP 和冗余攻击，表明我们方法的通用性质和强度。详细的布局级评估表明，在百万门 ORPSoC 设计中，UNSAIL 的面积和功率开销分别为 0.26% 和 0.61%。