论文标题
拥抱图形神经网络以进行硬件安全性（受邀纸）
Embracing Graph Neural Networks for Hardware Security (Invited Paper)
论文作者
论文摘要
图形神经网络（GNN）由于对图形结构数据的深入学习而引起了越来越多的关注。 GNN在社交网络，化学和电子设计自动化（EDA）等各个领域取得了成功。电子电路具有悠久的形式历史，毫不奇怪，GNNS在解决各种EDA任务方面表现出了最先进的表现。更重要的是，现在使用GNN来解决一些硬件安全问题，例如检测知识产权（IP）盗版和硬件特洛伊木马（HTS），仅举几例。 在这项调查中，我们首先概述了GNN在硬件安全性中的使用情况，并提出了第一个将基于最先进的GNN的硬件安全系统划分为四类分类法，将其分为四类：（i）HT检测系统，（ii）IP PIRACY检测系统，（III）逆向工程平台和（IV）逆向工程平台，以及（IV）攻击logigic logic oficiCIcic oficIcic oficiCICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICICIC。我们总结了所采用的GNN的不同体系结构，图形类型，节点功能，基准数据集和模型评估。最后，我们详细介绍了所学的教训并讨论未来的方向。
Graph neural networks (GNNs) have attracted increasing attention due to their superior performance in deep learning on graph-structured data. GNNs have succeeded across various domains such as social networks, chemistry, and electronic design automation (EDA). Electronic circuits have a long history of being represented as graphs, and to no surprise, GNNs have demonstrated state-of-the-art performance in solving various EDA tasks. More importantly, GNNs are now employed to address several hardware security problems, such as detecting intellectual property (IP) piracy and hardware Trojans (HTs), to name a few. In this survey, we first provide a comprehensive overview of the usage of GNNs in hardware security and propose the first taxonomy to divide the state-of-the-art GNN-based hardware security systems into four categories: (i) HT detection systems, (ii) IP piracy detection systems, (iii) reverse engineering platforms, and (iv) attacks on logic locking. We summarize the different architectures, graph types, node features, benchmark data sets, and model evaluation of the employed GNNs. Finally, we elaborate on the lessons learned and discuss future directions.
