Knowledge Graph Embedding and Visualization for Pre-Silicon Detection of Hardware Trojans
Dmitry Utyamishev, Inna Partin-Vaisband · 2022
While financially preferable, pre-silicon hardware Trojan (HT) detection remains a primary security challenge in modern integrated circuits (ICs) In this paper, a pre-silicon framework is developed for identifying rarely triggered nets (including those of HTs) Unsupervised knowledge graph embedding is utilized to transform the conditional triggering probability of IC nets into the Euclidean distance between the nets’ embeddings The proposed approach is not limited by HT types/IC sizes and is reference-free The framework is evaluated with TrustHub benchmarks, fully supporting the theoretical results HTs are identified in the center of the embeddings’ cloud, reducing the HT search space by over 10X.