GNN4IP: Graph Neural Network for Hardware Intellectual Property Piracy Detection
GNN4IP: Graph Neural Network for Hardware Intellectual Property Piracy Detection
复制标题
GNN4IP：用于硬件知识产权盗版检测的图神经网络
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
M. A. Faruque
中科院分区：
文献类型：
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作者：
Rozhin Yasaei;Shi Yu;Emad Kasaeyan Naeini;M. A. Faruque
严格的上市时间限制以及巨大的硬件设计和制造成本将半导体行业推向了硬件知识产权（IP）核心设计。然而，集成电路（IC）供应链的全球化使知识产权提供商面临知识产权被盗和非法再分配的风险。提出了水印和指纹检测IP盗版的方法。然而，它们带来了额外的硬件开销，并且不能保证IP安全性，因为据报道，高级攻击可以删除水印、伪造或绕过它。在这项工作中，我们提出了一种新的方法，GNN4IP，来评估电路之间的相似性并检测IP盗版。我们将硬件设计建模为一个图，并使用我们收集的寄存器传输级代码和门级网络列表的综合数据集构建一个图神经网络模型来学习其行为。GNN4IP在我们的数据集中以96%的准确率检测IP盗版，并以100%的准确率识别混淆版本的原始IP。
Aggressive time-to-market constraints and enormous hardware design and fabrication costs have pushed the semiconductor industry toward hardware Intellectual Properties (IP) core design. However, the globalization of the integrated circuits (IC) supply chain exposes IP providers to theft and illegal redistribution of IPs. Watermarking and fingerprinting are proposed to detect IP piracy. Nevertheless, they come with additional hardware overhead and cannot guarantee IP security as advanced attacks are reported to remove the watermark, forge, or bypass it. In this work, we propose a novel methodology, GNN4IP, to assess similarities between circuits and detect IP piracy. We model the hardware design as a graph and construct a graph neural network model to learn its behavior using the comprehensive dataset of register transfer level codes and gate-level netlists that we have gathered. GNN4IP detects IP piracy with 96% accuracy in our dataset and recognizes the original IP in its obfuscated version with 100% accuracy.
DOI：
10.1109/tnnls.2020.2978386
发表时间：
2021-01-01
影响因子：
10.4
作者：
Wu, Zonghan;Pan, Shirui;Yu, Philip S.
通讯作者：
Yu, Philip S.