赵聪慧,,
刘燕江
战略支援部队信息工程大学 郑州 450000
详细信息
作者简介:严迎建：男，1973年生，教授，研究方向为安全专用芯片设计技术等
赵聪慧：女，1995年生，硕士生，研究方向为安全专用芯片设计与防护
刘燕江：男，1990年生，讲师，研究方向为硬件木马检测、安全专用芯片设计技术等
通讯作者:赵聪慧 1024600921@qq.com
中图分类号:TN918; TP309+.1计量
文章访问数:283
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被引次数:0
出版历程
收稿日期:2021-01-04
修回日期:2021-03-10
网络出版日期:2021-06-24
刊出日期:2021-08-10
Hardware Trojan Detection Based on Multiple Structural Features
Yingjian YAN,Conghui ZHAO,,
Yanjiang LIU
Strtegic Support Force Information Engineering University, Zhengzhou 450000, China
摘要
摘要:硬件木马是第三方知识产权(IP)核的主要安全威胁，现有的安全性分析方法提取的特征过于单一，导致特征分布不够均衡，极易出现较高的误识别率。该文提出了基于有向图的门级网表抽象化建模算法，建立了门级网表的有向图模型，简化了电路分析流程；分析了硬件木马共性特征，基于有向图建立了涵盖扇入单元数、扇入触发器数、扇出触发器数、输入拓扑深度、输出拓扑深度、多路选择器和反相器数量等多维度硬件木马结构特征；提出了基于最近邻不平衡数据分类(SMOTEENN)算法的硬件木马特征扩展算法，有效解决了样本特征集较少的问题，利用支持向量机建立硬件木马检测模型并识别出硬件木马的特征。该文基于Trust_Hub硬件木马库开展方法验证实验，准确率高达97.02%，与现有文献相比真正类率(TPR)提高了13.80%，真负类率(TNR)和分类准确率(ACC)分别提高了0.92%和2.48%，在保证低假阳性率的基础上有效识别硬件木马。
关键词:硬件木马检测/
IP核/
有向图/
结构特征/
支持向量机
Abstract:Hardware Trojans are the main security threats of the third-party Intellectual Property (IP) cores. The existing pre-silicon hardware Trojan detection methods are difficult to be used in a large amount of hardware Trojans detection and the detection accuracy is hard to be enhanced. A gate-level netlist abstract modeling algorithm is proposed to reduce the cost of trustworthiness analysis method, which establishes a directed graph of the gate-level netlist and stores the graph data into the crosslinked list. Furthermore, the characteristics of hardware Trojans are analyzed in the view of the attacker view and a 7-dimensional feature vector based on the directed graph is proposed. Moreover, a hardware Trojan feature extraction algorithm is proposed to extract the 7-dimensional feature of the gate-level netlist, and a Trojan feature expansion algorithm based on the Synthetic Minority Oversampling Technique and Edited Nearest Neighbor (SMOTEENN) is introduced to expand the number of Trojan samples and the Support Vector Machine (SVM) algorithm is utilized to identify the existence of hardware Trojan. 15 benchmark circuits from the Trust-hub are used to validate the efficacy of the proposed approach and the accuracy rate we achieved is 97.02%. True Positive Rate (TPR) is increased by 13.80%, True Negative Rate (TNR) and ACCuracy (ACC) is increased by 0.92% and 2.48% respectively compared with the existing reference.
Key words:Hardware Trojan detection/
Intellectual Property (IP) core/
Directed graph/
Structural feature/
Support Vector Machine (SVM)
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