用图神经网络学习门级网表的结构可操控性，发现隐藏的硬件安全特征。
Learning Structural Manipulability in Gate-Level Netlists Using Graph Neural Networks
- 将网表建模为有向图，用GNN预测节点级结构灵活性得分。
- 在ISCAS85和EPFL数据集上，层次化GNN模型排名最稳定。
- 可识别植入木马电路的结构差异，适合硬件安全研究者使用。
门级网表具有独立于功能仿真影响信号传播的内在结构特性。本文定义了一种拓扑驱动的结构可操控性评分，通过路径参与度、k-core嵌入、对称性和中心性来刻画节点级结构灵活性。将网表建模为有向图，采用图神经网络（GNN）进行节点级回归以学习该拓扑衍生评分。在ISCAS85和EPFL基准上的实验评估了不同GNN架构在未见电路上的逼近效果，其中层次化模型表现最优。组件级和消融分析揭示了各因素的贡献。以TrustHub模板注入木马的案例研究显示，拓扑评分能识别出统计上可区分的结构模式，表明该方法提供互补的结构洞察。
原文摘要 · Abstract (English)
Gate-level netlists exhibit intrinsic structural properties that influence signal propagation independently of functional simulation. We define a topology-driven structural manipulability score that characterizes node-level structural flexibility using path participation, k-core embedding, symmetry, and centrality. Modeling netlists as directed graphs, we formulate node-level regression to learn this topology-derived score using graph neural networks (GNNs). Experiments on ISCAS85 and EPFL benchmarks evaluate how effectively different GNN architectures approximate this metric across held-out circuits, with hierarchical models yielding the most consistent rankings. Component-level and ablation analyses examine the contribution of individual factors. As an illustrative case study, analysis of Trojan-injected circuits using TrustHub templates reveals statistically distinguishable structural patterns, indicating that topology-based scoring provides complementary structural insight.
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