利用图神经网络实现硬件安全（特邀论文）
Embracing Graph Neural Networks for Hardware Security (Invited Paper)
摘要：
图神经网络（GNN）由于其在图结构数据的深度学习中的优越性能而引起了越来越多的关注。GNN在各个领域都取得了成功，例如社交网络、化学和电子设计自动化（EDA）。电子电路被表示为图形有着悠久的历史，并且毫不奇怪，GNN在解决各种EDA任务方面表现出了最先进的性能。更重要的是，GNN现在被用来解决几个硬件安全问题，例如检测知识产权（IP）盗版和硬件木马（HT）等。在本次调查中，我们首先全面概述了GNN在硬件安全中的使用，并提出了第一个分类法，将最先进的基于GNN的硬件安全系统分为四类：（i）HT检测系统，（ii）IP盗版检测系统，（iii）逆向工程平台，以及（iv）对逻辑锁定的攻击。我们总结了不同的架构，图形类型，节点功能，基准数据集和模型评估的GNN。最后，我们阐述了经验教训，并讨论了未来的方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
