Hardware Trojan Detection Utilizing Graph Neural Networks and Structural Checking

The integrated circuit (IC) industry has experienced exponential growth in the complexity and scale of hardware designs. To sustain this growth, faster development cycles and cost-effective solutions have been the focus of many companies, notably through the incorporation of third-party intellectual property (IP). Outsourcing the production of sub-components reduces development time and enables faster time-to-market; however, this approach also introduces the threat of Hardware Trojans, which are malicious modifications or additions to an IC, posing significant security risks due to their small size, low activation frequency, and complex obfuscation techniques. This research proposes an advancement to the Trojan detection mechanisms incorporated in the Structural Checking Tool, a Trojan detection tool that focuses on the identification of logical Trojans embedded within soft IPs. Leveraging graph structures generated by the tool and signal-level features, this research develops a new dataset and three graph neural network architectures. Each neural network corresponds to classical graph neural network layers and execute graph-level probabilistic binary classification of Trojan inclusion. Through rigorous testing with two potential sets of node-level feature vectors, this research offers a faster, more accurate, and more adaptable approach than those existing within the current tool.