Date of Graduation


Document Type


Degree Name

Master of Science in Computer Science (MS)

Degree Level



Computer Science & Computer Engineering


Jia Di

Committee Member

Alexander Nelson

Second Committee Member

Miaoqing Huang


Analysis, Detection, Hardware, RTL, Trojan


With the rising complexity and size of hardware designs, saving development time and cost by employing third-party intellectual property (IP) into various first-party designs has become a necessity. However, using third-party IPs introduces the risk of adding malicious behavior to the design, including hardware Trojans. Different from software Trojan detection, the detection of hardware Trojans in an efficient and cost-effective manner is an ongoing area of study and has significant complexities depending on the development stage where Trojan detection is leveraged. Therefore, this thesis research proposes improvements to various components of the soft IP analysis methodology utilized by the Structural Checking Tool. The Structural Checking Tool analyzes the register-transfer level (RTL) code of IPs to determine their functionalities and to detect and identify hardware Trojans inserted. The Structural Checking process entails parsing a design to yield a structural representation and assigning assets that encompass 12 different characteristics to the primary ports and internal signals. With coarse-grained asset reassignment based on external and internal signal connections, matching can be performed against trusted IPs to classify the functionality of an unknown soft IP. Further analysis is done using a Golden Reference Library (GRL) containing information about known Trojan-free and Trojan-infested designs and serves as a vital component for unknown soft IP comparison. Following functional identification, the unknown soft IP is run through a fine-grained reassignment strategy to ensure usage of up-to-date GRL assets, and then the matching process is used to determine whether said IP is Trojan-infested or Trojan-free. This necessitates a large GRL while maintaining a balance of computational resources and high accuracy to ensure effective matching.