Challenging the Security of Logic Locking Schemes in the Era of Deep Learning: A Neuroevolutionary Approach
Challenging the Security of Logic Locking Schemes in the Era of Deep Learning: A Neuroevolutionary Approach
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挑战深度学习时代逻辑锁定方案的安全性：神经进化方法
DOI：
10.1145/3431389
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
R. Leupers
中科院分区：
文献类型：
--
作者：
Dominik Sisejkovic;Farhad Merchant;Lennart M. Reimann;Harshit Srivastava;Ahmed Hallawa;R. Leupers
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重要技术。然而，近年来，各种去混淆攻击的引入彻底挑战了锁定方案的安全性。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，我们提出了SnapShot，这是一种对逻辑锁定的新型攻击，它是第一种利用人工神经网络直接从锁定的合成门级网表预测关键位值而不使用黄金参考的攻击。因此，与现有工作相比，该攻击使用了更简单但更灵活的学习模型。两种不同的方法进行评估。第一种方法是基于一个简单的前馈全连接神经网络。第二种方法利用遗传算法来进化更复杂的卷积神经网络架构，专门用于给定的任务。攻击流程提供了一个通用的和可定制的框架，用于使用机器学习技术攻击锁定方案。我们对SnapShot进行了广泛的评估，用于两种现实的攻击场景，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在选定的攻击场景中实现了82.60%的平均关键预测准确率，与最先进的技术相比，性能显著提高了10.49个百分点。此外，SnapShot在所有评估基准上都优于现有技术。结果表明，常见的逻辑锁方案的安全基础是建立在可疑的假设。根据经验教训，我们讨论了SnapShot发现的逻辑锁定的漏洞和潜力。这些结论为设计未来逻辑锁定方案的挑战提供了见解，这些方案可以抵御机器学习攻击。
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.
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