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.

翻译：图表神经网络(GNNs)由于在图表结构数据方面的深层学习表现优异,吸引了越来越多的关注。全球NNs在社交网络、化学和电子设计自动化(EDA)等不同领域取得了成功。电子电路长期以来一直以图表形式出现,毫不奇怪,全球NNs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GNS来解决若干硬件安全问题,例如发现知识产权(IP)盗版和硬件Trojans(HTs)等。在本调查中,我们首先对GNNs在硬件安全方面的使用情况作了全面的概述,并提出了第一个分类法,将最先进的GNN硬件安全系统分为四类:(一) HT探测系统,(二) IP盗版探测系统,(三) 反向工程平台,以及(四) 逻辑锁定攻击。我们总结了不同结构、图表类型、节点特征、基准数据集和模型评估。最后,我们详细阐述了所使用GNNS的教训和未来方向。