Hardware Trojans Detection Through RTL Features Extraction and Machine Learning
Hardware Trojans Detection Through RTL Features Extraction and Machine Learning
复制标题
通过RTL特征提取和机器学习检测硬件木马
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Xin Chen
中科院分区：
文献类型：
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作者：
Jizhong Yang;Ying Zhang;Yifeng Hua;Jiaqi Yao;Z. Mao;Xin Chen
为了保证集成电路的硬件安全，特别是考虑到第三方IP核（3PIP核）在SoC设计中的应用，本文提出了一种针对寄存器传输级（RTL）描述的硬件木马（HT）检测方案。通过分析可疑电路的RTL设计的结构和信号特征，构建RTL节点的数学模型，以实现与HT相关并可用于机器学习（ML）的数字特征。然后通过随机森林算法训练针对某一类电路的机器学习分类器并提取数值特征。在实验中应用22个电路进行训练，22个电路进行检测，结果表明我们提出的检测方法的平均HT检测率可以达到99.93%。
To ensure the hardware security of integrated circuits, especially considering third-party IP cores (3PIP cores) application in SoC design, a novel Hardware Trojan (HT) detection scheme aimed to Register Transfer Level (RTL) Description is proposed in this paper. By analyzing the structural and signal characteristics of RTL design for suspicious circuits, a mathematical model of RTL nodes is constructed to achieve numeric features relevant to HT and available for Machine Learning (ML). Then a ML classifier for a certain category of circuits is trained by Random Forest algorithm and numerical features are extracted. 22 circuits are applied to training and 22 circuits to detecting in experiments, the results show that the average HT detection rate of our proposed detection method can reach 99.93%.