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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.15037 |
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| _version_ | 1866915681142833152 |
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| author | Pan, Weitao Dong, Meng Qiu, Zhiliang Yang, Jianlei Di, Zhixiong Gao, Yiming |
| author_facet | Pan, Weitao Dong, Meng Qiu, Zhiliang Yang, Jianlei Di, Zhixiong Gao, Yiming |
| contents | Reverse engineering of gate-level netlist is critical for Hardware Trojans detection and Design Piracy counteracting. The primary task of gate-level reverse engineering is to separate the control and data signals from the netlist, which is mainly realized by identifying state registers with topological comparison.However, these methods become inefficient for large scale netlist. In this work, we propose RELIC-GNN, a graph neural network based state registers identification method, to address these issues. RELIC-GNN models the path structure of register as a graph and generates corresponding representation by considering node attributes and graph structure during training. The trained GNN model could be adopted to find the registers type very efficiently. Experimental results show that RELIC-GNN could achieve 100% in recall, 30.49% in precision and 88.37% in accuracy on average across different designs, which obtains significant improvements than previous approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15037 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RELIC-GNN: Efficient State Registers Identification with Graph Neural Network for Reverse Engineering Pan, Weitao Dong, Meng Qiu, Zhiliang Yang, Jianlei Di, Zhixiong Gao, Yiming Cryptography and Security Reverse engineering of gate-level netlist is critical for Hardware Trojans detection and Design Piracy counteracting. The primary task of gate-level reverse engineering is to separate the control and data signals from the netlist, which is mainly realized by identifying state registers with topological comparison.However, these methods become inefficient for large scale netlist. In this work, we propose RELIC-GNN, a graph neural network based state registers identification method, to address these issues. RELIC-GNN models the path structure of register as a graph and generates corresponding representation by considering node attributes and graph structure during training. The trained GNN model could be adopted to find the registers type very efficiently. Experimental results show that RELIC-GNN could achieve 100% in recall, 30.49% in precision and 88.37% in accuracy on average across different designs, which obtains significant improvements than previous approaches. |
| title | RELIC-GNN: Efficient State Registers Identification with Graph Neural Network for Reverse Engineering |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2512.15037 |