Hardware Trojan detection method based upon XGBoost model

Abstract

This paper proposed a hardware Trojan detection method based upon XGBoost(eXtreme Gradient Boosting) model by using the analysis results of ring oscillator network, and used the cross-validation method to optimize the model. It can utilize that train sample dataset to build the XGBoost classification model, and use the supervised learn mode to classify the data, thus realizing the separation of the original circuit and the Trojan circuit. Using RS232-T100 and RS232-T800 as Trojan circuits, the FPGA experiment was carried out. The experimental results showed that the detection rate of the Trojan data with RO at 0.1 ms integration time is 100% and 99.20%, which verified the validity of the method. In addition, when compared with traditional methods and other machine learning methods, the XGBoost-based detection method shows a higher detection rate, and can analyze the characteristic importance of the multi-dimensional vector correlation data instead of dimensionality reduction. It can maximize the key features required for Trojan detection.

Keywords

• hardware Trojan
• XGBoost
• cross-validation
• ring oscillator
• FPGA