Logic locking is a prominent technique to protect the integrity of hardware designs throughout the integrated circuit design and fabrication flow. However, in recent years, the security of locking schemes has been thoroughly challenged by the introduction of various deobfuscation attacks. As in most research branches, deep learning is being introduced in the domain of logic locking as well. Therefore, in this article we present SnapShot, a novel attack on logic locking that is the first of its kind to utilize artificial neural networks to directly predict a key bit value from a locked synthesized gate-level netlist without using a golden reference. Hereby, the attack uses a simpler yet more flexible learning model compared to existing work. Two different approaches are evaluated. The first approach is based on a simple feedforward fully connected neural network. The second approach utilizes genetic algorithms to evolve more complex convolutional neural network architectures specialized for the given task. The attack flow offers a generic and customizable framework for attacking locking schemes using machine learning techniques. We perform an extensive evaluation of SnapShot for two realistic attack scenarios, comprising both reference combinational and sequential benchmark circuits as well as silicon-proven RISC-V core modules. The evaluation results show that SnapShot achieves an average key prediction accuracy of 82.60% for the selected attack scenario, with a significant performance increase of 10.49 percentage points compared to the state of the art. Moreover, SnapShot outperforms the existing technique on all evaluated benchmarks. The results indicate that the security foundation of common logic locking schemes is built on questionable assumptions. Based on the lessons learned, we discuss the vulnerabilities and potentials of logic locking uncovered by SnapShot. The conclusions offer insights into the challenges of designing future logic locking schemes that are resilient to machine learning attacks.

中文翻译：

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在深度学习时代挑战逻辑锁定方案的安全性：一种神经进化方法

逻辑锁定是在整个集成电路设计和制造流程中保护硬件设计完整性的一项突出技术。然而，近年来，由于各种反混淆攻击的引入，锁定方案的安全性受到了彻底的挑战。与大多数研究分支一样，深度学习也被引入逻辑锁定领域。因此，在本文中，我们介绍了 SnapShot，这是一种针对逻辑锁定的新型攻击，它是同类中第一个利用人工神经网络直接从锁定的合成门级网表中预测关键位值的方法，而无需使用黄金参考。因此，与现有工作相比，攻击使用了更简单但更灵活的学习模型。评估了两种不同的方法。第一种方法基于简单的前馈全连接神经网络。第二种方法利用遗传算法来发展更复杂的卷积神经网络架构，专门用于给定任务。攻击流程提供了一个通用且可定制的框架，用于使用机器学习技术攻击锁定方案。我们针对两种实际攻击场景对 SnapShot 进行了广泛评估，包括参考组合和顺序基准电路以及经过硅验证的 RISC-V 核心模块。评估结果表明，SnapShot 对选定的攻击场景实现了 82.60% 的平均密钥预测准确率，与现有技术相比，性能显着提升了 10.49 个百分点。而且，SnapShot 在所有评估基准上都优于现有技术。结果表明，常见逻辑锁定方案的安全基础是建立在有问题的假设之上的。基于经验教训，我们讨论了 SnapShot 发现的逻辑锁定的漏洞和潜力。这些结论提供了对设计对机器学习攻击具有弹性的未来逻辑锁定方案所面临的挑战的见解。