FAST-GO: Fast, Accurate, and Scalable Hardware Trojan Detection using Graph Convolutional Networks
Ali Imangholi, Mona Hashemi, Amirabbas Momeni, Siamak Mohammadi, Trevor E. Carlson · 2024
Technology advancements and the rising demand for integrated circuits have led designers to rely on third-party vendors for certain aspects of the production process. This outsourcing raises security concerns, including the risk of hardware Trojans during production. In this paper, we propose a machine learning model that utilizes graph convolutional networks to detect hardware Trojans in gate-level netlists. In this regard, the proposed model, FAST-GO, is trained based on the extracted graph from the netlist and presents an efficient and compact set of structural gate-level features to classify the graph nodes as Trojan or benign nodes. Utilizing a small number of features in the presence of an efficient dataset enables the model to boost scalability and makes it a sensitive model applicable for large netlists. Experimental evaluation shows that our model has been successful in detecting 95.38% of Trojan nodes in 15 different circuits from the Trust-Hub benchmark only in0.84 seconds on average using an Intel Core i5-3230M 2.6 GHz Processor with 6 GB RAM. This shows that FAST-GO is a high-performance accurate detection method and is able to consider all known HT detection mechanisms.