基于木马特征风险敏感的硬件木马检测方法  

Hardware Trojan detection method based upon Trojan cost-sensitive

作　　者：李林源 徐金甫[1] 严迎建[1] 刘燕江 Li Linyuan;Xu Jinfu;Yan Yingjian;Liu Yanjiang(Key Laboratory of Information Security,Information Engineering University,Zhengzhou 450000,China)

机构地区：[1]信息工程大学信息安全重点实验室,河南郑州450000

出　　处：《电子技术应用》2023年第6期35-43,共9页Application of Electronic Technique

摘　　要：针对现有硬件木马检测方法中存在的木马检出率偏低问题,提出一种基于木马特征风险敏感的门级硬件木马检测方法。通过分析木马电路的结构特征和信号特征,构建11维硬件木马特征向量;提出了基于BorderlineSMOTE的硬件木马特征扩展算法,有效扩充了训练数据集中的木马样本信息;基于PSO智能寻优算法优化SVM模型参数,建立了木马特征风险敏感分类模型。该方法基于Trust-Hub木马库中的17个基准电路展开实验验证,其中16个基准电路的平均真阳率(TPR)达到100%,平均真阴率(TNR)高达99.04%,与现有的其他检测方法相比,大幅提升了硬件木马检出率。In the existing hardware Trojan detection methods,there is problem of low detection rate.Therefore,a cost-sensitive hardware Trojan detection was proposed.By analyzing the structural and signal features of Trojan circuits,an 11 dimensional Tro‐jan feature vector was established.A Trojan feature expansion algorithm based on Borderline-SMOTE was proposed,which effec‐tively expanded the Trojan sample information in the training set.Based on PSO algorithm,the parameters of SVM model were optimized,and a cost-sensitive classification model was established.17 benchmark circuits from the Trust-Hub were used to verify the efficacy of the proposed approach.Among them,the TPR of 16 benchmark circuits is 100%,and the average TNR is as high as 99.04%.Compared with other existing methods,the detection rate of Trojan is improved greatly.

关 键 词：硬件木马检测 风险敏感 PSO SVM分类模型 

分 类 号：TP309.1[自动化与计算机技术—计算机系统结构]