基于路径特征和支持向量机算法的硬件木马检测技术

Hardware Trojan Detection Based on Path Feature and Support Vector Machine

硬件木马攻击成为当前集成电路(IC)面临的严重威胁。针对硬件木马电路具有隐蔽、不易触发以及数据集不均衡等特点,该文提出对门级网表进行静态分析的硬件木马检测技术。基于电路可测性原理建立涵盖节点扇入数、逻辑门距离、路径数、节点扇出数的硬件木马路径特征,简化特征分析流程;基于提取的路径特征,使用支持向量机(SVM)算法区分电路中的木马节点和正常节点。提出训练集双重加权技术,解决数据集不均衡问题,提升分类器的性能。实验结果表明,分类器可以用于电路中的可疑节点检测,准确率(ACC)达到99.85%;训练集静态加权有效提升分类器性能,准确率(ACC)提升5.58%;与现有文献相比,以36%的特征量,真阳性率(TPR)降低1.07%,真阴性率(TNR)提升2.74%,准确率(ACC)提升2.92%。该文验证了路径特征和SVM算法在硬件木马检测中的有效性,明确了数据集均衡性与检测性能的关系。

Hardware Trojan attack has become a serious threat to Integrated Circuit(IC).Hardware Trojans are hidden,rare triggered and the data-sets of Trojan benchmarks are unbalanced,a hardware Trojan detection method that performs a static analysis in gate-level netlist is presented.The path-feature based on the principle of design-for-test is proposed to simplify the analysis of feature.Based on the path-feature extracted in a circuit,the nets are classified into two groups with the Support Vector Machine(SVM)machine learning.It uses the double-weighting method of training-set to improve the performance of the classifier.Experimental results demonstrate that this method can be used to detect the suspicious nets in circuits and the ACCuracy(ACC)can achieve up to 99.85%.The static weighting method improves the performance of the classifier and the improvement of accuracy can achieve up 5.58%.Compared with the existing reference,the size of feature is only 36%,True Positive Rate(TPR)is decreased by 1.07%,True Negative Rate(TNR)is increased by 2.74%and ACC is increased by 2.92%respectively.This work verifies the efficiency of path-feature and SVM machine learning for Hardware Trojan detection and clarifies the relationship between the balance of data-sets and the detection performance.