Cybersecurity has become a serious concern in our daily lives. The malicious functions inserted into hardware devices have been well known as hardware Trojans. In this letter, we propose a hardware-Trojan classification method at gate-level netlists utilizing boundary net structures. We first use a machine-learning-based hardware-Trojan detection method and classify the nets in a given netlist into a set of normal nets and a set of Trojan nets. Based on the classification results, we investigate the net structures around the boundary between normal nets and Trojan nets, and extract the features of the nets mistakenly identified to be normal nets or Trojan nets. Finally, based on the extracted features of the boundary nets, we again classify the nets in a given netlist into a set of normal nets and a set of Trojan nets. The experimental results demonstrate that our proposed method outperforms an existing machine-learning-based hardware-Trojan detection method in terms of its true positive rate.
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Artificial Intelligence