【摘要】 在环形振荡器网络分析的基础上,提出一种基于XGBoost的硬件木马检测方法,并利用交叉验证方法进行模型优化。该方法能够利用训练样本数据集构建XGBoost分类模型,采用监督学习模式对数据进行分类,从而实现将原始电路与木马电路分离的目的。以RS232-T100、RS232-T800为木马电路,进行FPGA实验,实验结果表明:对RO在0. 1 ms积分时间下的木马数据,检测率达到100%、 99. 20%,验证了本方法的有效性。此外,在与传统方法和其他机器学习方法比较时,基于XGBoost的检测方法表现出了更高的检测率,能对多维度向量的关联数据作特征重要性分析,而非降维,能最大限度地保留木马检测所需的关键特征。更多还原

【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 experi-ment 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 character-istic importance of the multi-dimensional vector correlation data instead of dimensionality reduction. It can maximize the key fea-tures required for Trojan detection.更多还原