2022/08/17 by Lilas Alrahis, Alrahis, Lilas, Satwik Patnaik +5
Computer Science · Engineering · #Advanced Memory and Neural Computing #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Physical Unclonable Functions (PUFs) and Hardware Security #VLSI and Analog Circuit Testing
paper · pdf · doi:10.48550/arxiv.2208.08554
openalex publication_date 2022/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.