融入环形振荡器木马特征的无监督硬件木马检测

机器学习用于集成电路硬件木马的检测可以有效提高检测率。无监督学习方法在特征选择上还存在不足,目前研究工作主要集中于有监督学习方法。文章引入环形振荡器木马的新特征,研究基于无监督机器学习的硬件木马检测方法。首先针对待测电路网表,提取每个节点的5维特征值,然后利用局部离群因子(LOF)算法计算各节点的LOF值,筛选出硬件木马节点。对Trust-HUB基准电路的仿真实验结果表明,该方法用于网表级电路硬件木马的检测,与现有基于无监督学习的检测方法相比,TPR(真阳性率)、P(精度)和F(度量)分别提升了16.19%、10.79%和15.56%。针对Trust-HUB基准电路的硬件木马检测的平均TPR、TNR和A,分别达到了58.61%、97.09%和95.60%。

A Method of Detecting Unsupervised Learning Hardware Trojan Incorporating Ring Oscillator Trojan Characteristics

Machine learning for integrated circuit hardware Trojan horse detection can effectively improve the detection rate. Unsupervised learning methods still have shortcomings in feature selection. At present, the research work mainly focuses on supervised learning methods. In this paper, the new characteristics of ring oscillator Trojan horse was introduced, and the hardware Trojan horse detection method based on unsupervised machine learning was studied. Firstly, the 5-Dimensional eigenvalues of each node were extracted for the circuit netlist to be tested. Then the local outlier factor of each node was calculated by LOF algorithm to screen out the hardware Trojan horse nodes. The simulation results of Trust-HUB reference circuit show that compared with the existing detection methods based on unsupervised learning, TPR (true positive rate), P (accuracy) and F (measurement) are improved by 16.19%, 10.79% and 15.56% respectively. The average TPR, TNR and A of hardware Trojan horse detection for Trust-HUB reference circuit reach 58.61%, 97.09% and 95.60% respectively.