GNN4Gate: A Bi-Directional Graph Neural Network for Gate-Level Hardware Trojan Detection
Dong Cheng1,a, Chen Dong1,4,b, Wenwu He2,c, Zhenyi Chen3,d and Yi Xu1,e
1College of Computer and Data Science, Fuzhou University, Fuzhou, China
2School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, China
3Department of Computer Science and Engineering, University of South Floride, Tampa, USA
4Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, China
achengdong2021@foxmail.com
bdongchen@fzu.edu.cn
chwwhbb@163.com
dzhenyichen@usf.edu
exuyilaser@foxmail.com
ABSTRACT
Hardware is the physical foundation of cyberspace, and chips are the core components. The security risk of the chip will bring disaster to the entire world. Hardware Trojans (HTs) are malicious circuits, which are the primary security issue of chip. Recently, a series of machine learning-based HT detection methods were proposed. However, some shortcomings still deserve further consideration, such as relying too much on manual feature extraction, losing some signal propagation structure information, being hard to track the HTs’ location and adapt them to various types of HTs. To address the above challenges, this paper proposes a gate-level HT detection method based on Graph Neural Network (GNN), named GNN4Gate, which is a goldenfree Trojan-gate identification technology. Specifically, a special coding method combining logic gate type and port connection information is developed for circuit graph modeling. Based on this, taking logic gates as the classification object, an automatic GNN detection architecture based on Bi-directional Graph Convolutional Network (Bi-GCN) is developed to aggregate both the circuit signal propagation (forward) and dispersion (backward) structure features from the circuit graph. The proposed method is evaluated by Trusthub benchmarks with different functional HTs, the average True Positive Rate (Recall) is 87.14%, and the average True Negative Rate is 99.73%. The experimental results demonstrate that GNN4Gate is sufficiently accurate compared to the state-of-the-art detection works at gate-level.
Keywords: Hardware Trojan, Static Detection, Gate-Level, Trojan-Gate, Graph Neural Network, Golden-free.
