A Hardware-Trojans Detection Approach Based on eXtreme Gradient Boosting
2019 IEEE 2nd International Conference on Computer and Communication Engineering Technology (CCET)(2019)
摘要
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 pre-silicon detection method. In this paper, we propose a machine-learning-based hardware-Trojans detection method in gate-level. 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.
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关键词
hardware trojan,gate-level netlist,machine learning,eXtreme gradient boosting algorithm
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