| Title | Hardware Trojan Detection Combine with Machine Learning: an SVM-based Detection Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Hu, Taifeng, Wu, Liji, Zhang, Xiangmin, Yin, Yanzhao, Yang, Yijun |
| Conference Name | 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID) |
| Keywords | composability, field programmable gate arrays, Hardware, hardware trojan, hardware Trojan detection, IC, integrated circuits, invasive software, Kernel, learning (artificial intelligence), machine learning, Microelectronics Security, Predictive Metrics, pubcrawl, Resiliency, SAKURA-G circuit board, security, side-channel analysis, side-channel analysis (SCA), support vector machine (SVM), support vector machine classifier, Support vector machines, SVM-based detection approach, Training, Trojan detection, Trojan detection rate, Trojan horses, Xilinx SPARTAN-6 |
| Abstract | With the application of integrated circuits (ICs) appears in all aspects of life, whether an IC is security and reliable has caused increasing worry which is of significant necessity. An attacker can achieve the malicious purpose by adding or removing some modules, so called hardware Trojans (HTs). In this paper, we use side-channel analysis (SCA) and support vector machine (SVM) classifier to determine whether there is a Trojan in the circuit. We use SAKURA-G circuit board with Xilinx SPARTAN-6 to complete our experiment. Results show that the Trojan detection rate is up to 93% and the classification accuracy is up to 91.8475%. |
| DOI | 10.1109/ICASID.2019.8924992 |
| Citation Key | hu_hardware_2019 |
Hardware Trojan Detection Combine with Machine Learning: an SVM-based Detection Approach