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Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs.

, , and . ASP-DAC, page 83-90. ACM, (2023)

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PoisonedGNN: Backdoor Attack on Graph Neural Networks-based Hardware Security Systems., , , , and . CoRR, (2023)Security Closure of IC Layouts Against Hardware Trojans., , , , , , , , , and . CoRR, (2022)UNSAIL: Thwarting Oracle-Less Machine Learning Attacks on Logic Locking., , , , , , and . CoRR, (2020)ScanSAT: Unlocking Static and Dynamic Scan Obfuscation., , , , , , and . IEEE Trans. Emerg. Top. Comput., 9 (4): 1867-1882 (2021)Always be Pre-Training: Representation Learning for Network Intrusion Detection with GNNs., , , and . CoRR, (2024)UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction., , , , and . ICCAD, page 1-9. IEEE, (2021)TrojanSAINT: Gate-Level Netlist Sampling-Based Inductive Learning for Hardware Trojan Detection., , , and . ISCAS, page 1-5. IEEE, (2023)ShapeShifter: Protecting FPGAs from Side-Channel Attacks with Isofunctional Heterogeneous Modules., , , and . IOLTS, page 1-7. IEEE, (2023)Titan: Security Analysis of Large-Scale Hardware Obfuscation Using Graph Neural Networks., , , , and . IEEE Trans. Inf. Forensics Secur., (2023)MaliGNNoma: GNN-Based Malicious Circuit Classifier for Secure Cloud FPGAs., , , , , , and . CoRR, (2024)