The globalization of the Integrated Circuit (IC) supply chain has moved most of the design, fabrication, and testing process from a single trusted entity to various untrusted third-party entities worldwide. The risk of using untrusted third-Party Intellectual Property (3PIP) is the possibility for adversaries to insert malicious modifications known as Hardware Trojans (HTs). These HTs can compromise the integrity, deteriorate the performance, deny the service, and alter the functionality of the design. While numerous HT detection methods have been proposed in the literature, the crucial task of HT localization is overlooked. Moreover, a few existing HT localization methods have several weaknesses: reliance on a golden reference, inability to generalize for all types of HT, lack of scalability, low localization resolution, and manual feature engineering/property definition. To overcome their shortcomings, we propose a novel, golden reference-free HT localization method at the pre-silicon stage by leveraging Graph Convolutional Network (GCN). In this work, we convert the circuit design to its intrinsic data structure, graph and extract the node attributes. Afterward, the graph convolution performs automatic feature extraction for nodes to classify the nodes as Trojan or benign. Our automated approach does not burden the designer with manual code review. It locates the Trojan signals with 99.6% accuracy, 93.1% F1-score, and a false-positive rate below 0.009%.

翻译：集成电路供应链的全球化将大多数设计、制造和测试过程从单一受信任的实体转移到全世界各种不受信任的第三方实体。使用不受信任的第三方知识产权(3PIP)的风险在于对手有可能插入恶意的修改,称为Hardware Trojans(HTs)。这些HT可能损害完整性、恶化性能、拒绝服务和改变设计功能。虽然文献中提出了许多HT检测方法,但HT本地化的关键任务被忽视。此外,现有的一些HT本地化方法存在一些弱点:依赖黄金参考,无法对所有类型的HT进行概括化(3PIP),缺乏可缩放性、低本地化分辨率以及手动地物工程/规格定义。为了克服这些缺点,我们提议在硅前阶段采用新颖的、无金色参考的HTL本地化方法,利用图层革命网络(GCN)。在这项工作中,我们将电路路路设计转换成其固有的数据结构、图表和节点属性。在随后,无法对各种类型的 HTRA1 进行自动地压分析,而没有将CROBRI 格式的温度分析。