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 paper 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 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 build on questionable assumptions. The conclusions of the evaluation offer insights into the challenges of designing future logic locking schemes that are resilient to machine learning attacks.
翻译: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突出技术。 但是,近年来,锁定计划的安全因引入各种腐蚀性攻击而受到了彻底的挑战。 正如大多数研究分支一样,深层学习也在逻辑锁定领域引入。 因此,我们在本文中介绍了 SnapShot: 对逻辑锁定的新攻击,这是首次使用人工神经网络直接预测从锁定的综合门级网络基准中的关键位值,而不使用黄金参考。 近些年来, 锁定计划的安全因引入了更简单、更灵活的学习模式, 与现有工作相比。 两种不同的方法都得到了评估。 第一个方法基于一个简单的向上推进完全连接的神经网络网络。 第二个方法利用基因算法来演进更复杂的脉动神经网络结构。 攻击流提供了一个通用和可定制的框架,用来使用机器学习技术来攻击锁定系统。 我们对SnastStencialShoperat两个现实攻击情景进行了广泛的评估, 包括参考逻辑的直线路路比, 以及SralShohoal-rode 逻辑模型的精确度评估, 以及Slavical-rial-rial-rial rodealal-revalalalalalalalal ladeal ladeal 学习, ladealal ladealalalalalalalal ladealalal lade, ladeal ladealalal disal ladeal dowd dowdal dowd dowdaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldald dowdaldaldaldaldaldaldaldaldaldaldaldaldaldald ow, owdaldaldaldaldaldaldaldaldaldaldaldald ressaldaldaldaldaldaldaldaldaldal madalal madaldaldaldaldaldaldaldaldaldaldaldaldaldaldaldalalalalalalalal masaldaldal masal