OMLA: An Oracle-Less Machine Learning-Based Attack on Logic Locking
OMLA: An Oracle-Less Machine Learning-Based Attack on Logic Locking
复制标题
OMLA：基于无 Oracle 机器学习的逻辑锁定攻击
DOI：
10.1109/tcsii.2021.3113035
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
O. Sinanoglu
中科院分区：
文献类型：
--
作者：
Lilas Alrahis;Satwik Patnaik;Muhammad Shafique;O. Sinanoglu
由于设计流程的全球化，对半导体供应链的基于硬件的攻击正在出现。逻辑锁定是一种为信任而设计的方案，承诺在整个供应链中提供保护。虽然攻击严重依赖于Oracle来打破逻辑锁定，但基于机器学习（ML）的攻击表明，即使没有Oracle，也有可能打破锁定。虽然非常强大，但当前基于ML的攻击只能恢复由锁定引入的转换的一个子集。我们的目标是通过开发一种称为OMLA的无预言图神经网络攻击来解决这个缺点，再次质疑逻辑锁定的安全性。我们在ISCAS-85和ITC-99基准测试上的实验表明，OMLA实现了高达97.22%的关键预测准确率，并且在所有评估的基准测试中都优于最先进的SnapShot和SAIL攻击。
Hardware-based attacks on the semiconductor supply chain are emerging due to the globalization of the design flow. Logic locking is a design-for-trust scheme that promises protection throughout the supply chain. While attacks have heavily relied on an oracle to break logic locking, machine learning (ML)-based attacks demonstrate the daunting possibility of breaking locking even without an oracle. Although very potent, current ML-based attacks recover only a subset of the transformations introduced by locking. We aim to address this shortcoming by developing an oracle-less graph neural network-based attack called OMLA, questioning once again the security of logic locking. Our experiments on ISCAS-85 and ITC-99 benchmarks demonstrate that OMLA achieves a key-prediction accuracy up to 97.22% and outperforms state-of-the-art SnapShot and SAIL attacks for all evaluated benchmarks.
TGA：针对逻辑锁定的无 Oracle 拓扑引导攻击
DOI：
10.1145/3338508.3359576
发表时间：
2019
期刊：
ASHES'19: Proceedings of the 3rd ACM Workshop on Attacks and Solutions in Hardware Security Workshop
影响因子：
--
作者：
Zhang, Yuqiao;Cui, Pinchen;Zhou, Ziqi;Guin, Ujjwal
通讯作者：
Guin, Ujjwal