SeGa: A Trojan Detection Method Combined With Gate Semantics
- Resource Type
- Conference
- Authors
- Ye, Yunying; Li, Shan; Shen, Haihua; Li, Huawei; Li, Xiaowei
- Source
- 2021 IEEE 30th Asian Test Symposium (ATS) ATS Asian Test Symposium (ATS), 2021 IEEE 30th. :43-48 Nov, 2021
- Subject
- Components, Circuits, Devices and Systems
Computing and Processing
Semantics
Neural networks
Electronics industry
Static analysis
Logic gates
Feature extraction
Hardware
Trojan detection
Gate embedding
NLP
- Language
- ISSN
- 2377-5386
Hardware Trojan has always been a major security threat to the integrated circuit industry. In this article, we propose a novel circuit gate embedding method called SeGa, which extracts the “semantic information” of gates in the netlist. The feature vectors that representing each type of gate extracted by SeGa are used as the inputs to the neural network classification model to detect Trojans. The experimental results on TRIT-TC benchmark show that SeGa can improve the performance of the neural network classification model to detect the Trojan gate sequence.