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Graph neural networks (GNNs) have attracted increasing attention due to theirsuperior performance in deep learning on graph-structured data. GNNs havesucceeded across various domains such as social networks, chemistry, andelectronic design automation (EDA). Electronic circuits have a long history ofbeing represented as graphs, and to no surprise, GNNs have demonstratedstate-of-the-art performance in solving various EDA tasks. More importantly,GNNs are now employed to address several hardware security problems, such asdetecting intellectual property (IP) piracy and hardware Trojans (HTs), to namea few.  In this survey, we first provide a comprehensive overview of the usage ofGNNs in hardware security and propose the first taxonomy to divide thestate-of-the-art GNN-based hardware security systems into four categories: (i)HT detection systems, (ii) IP piracy detection systems, (iii) reverseengineering platforms, and (iv) attacks on logic locking. We summarize thedifferent architectures, graph types, node features, benchmark data sets, andmodel evaluation of the employed GNNs. Finally, we elaborate on the lessonslearned and discuss future directions.

Embracing Graph Neural Networks for Hardware Security (Invited Paper)