A Hardware-Trojans Detection Approach Based on eXtreme Gradient Boosting

As the core component of the electronic devices, the integrated circuit (IC) must be taken seriously with its security. The pre-silicon detection methods do not require gold chips, are not affected by process noise and are suitable for the safe detection of a very large scale integration (VLSI). Therefore, more and more researchers are paying attention to the presilicon detection method. In this paper, we propose a machine-learning-based hardware-Trojans detection method in gatelevel. First, by the analysis of the Trojan circuits, we put forward new Trojan-net features. After that, we use the scoring mechanism of the eXtreme Gradient Boosting (XGBoost) to set up a new effective feature set of 49 out of 56 features. Finally, the hardware-Trojan classifier was trained based on the effective feature set. The experimental results show that the proposed method can obtain the average Recall of 89.84%, the average F-measure of 87.75% and the average Accuracy of 99.83%. Furthermore, through the comparison experiments, it is proved that the features proposed in this paper can further improve the performance of detection.