Hardware Trojan Detection Method Based on Random Forest

ZHANG Lei; YIN Meng-jie; WANG Jian-xin; DONG You-heng; XIAO Chao-en; ZHAO Cheng

Chinese

您当前的位置：

首页 >

文章列表页 >

Hardware Trojan Detection Method Based on Random Forest

更新时间：2026-09-04

- Hardware Trojan Detection Method Based on Random Forest
- Vol. 36, Issue 2, Pages: 83-87(2019)
- 作者机构：
  
  北京电子科技学院机构,北京,100070
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019
- 稿件说明：
移动端阅览
张磊, 殷梦婕, 王建新, 董有恒, 肖超恩, 刘东阳, 赵成. 基于随机森林的硬件木马检测方法[J]. 微电子学与计算机, 2019, 36(2): 83-87.

ZHANG Lei, YIN Meng-jie, WANG Jian-xin, et al. Hardware Trojan Detection Method Based on Random Forest[J]. 2019, 36(2): 83-87.
张磊, 殷梦婕, 王建新, 董有恒, 肖超恩, 刘东阳, 赵成. 基于随机森林的硬件木马检测方法[J]. 微电子学与计算机, 2019, 36(2): 83-87. DOI：

ZHANG Lei, YIN Meng-jie, WANG Jian-xin, et al. Hardware Trojan Detection Method Based on Random Forest[J]. 2019, 36(2): 83-87. DOI：

摘要

针对BP神经网络和SVM这两种机器学习算法中存在参数选择困难和时间开销较大的问题

本文提出了一种基于随机森林的硬件木马分类方法.首先

将硬件木马检测转化为二元分类问题

对芯片的能量消耗进行多次采样

再通过PCA对功耗曲线进行特征提取

最后利用随机森林分类模型对特征向量进行分类

达到检测硬件木马芯片的目的.实验结果表明

经PCA处理的相同硬件木马数据

随机森林的判别准确率与BP神经网络相比提高了9.13%

与SVM方法相比判别准确率提高了15.96%.而相比其他两种方法

时间开销也降低了8倍左右.

Abstract

Aiming at the problem of parameter selection and time overhead for BP neural network and SVM algorithms

this paper proposes a Hardware Trojan classification method based on Random Forests. Firstly

the Hardware Trojan detection problem is modeled as a binary classification problem and the power consumption of the chip is sampled several times. Then the characteristics of the power consumption curve are extracted by the PCA (principal component analysis). Finally

RF (Random Forests) classification model is used to classify the feature vectors in purpose of identifying Hardware Trojan chips. The experimental results show that

considering the same Hardware Trojan horse data processed by PCA

the discrimination accuracy of RF is improved by 9.13% compared with the BP neural network. Compared with the SVM(support vector machine) method

the discrimination accuracy is increased by 15.96%. Compared to the other two methods

the time cost of RF is reduced by about 8 times.

关键词

Keywords

references

Moein S, Subramnian J, Gulliver T A, et al. Classification of hardware trojan detection techniques[C]// Tenth International Conference on Computer Engineering % Systems. IEEE, 2016: 23-24.

董师师, 黄哲学.随机森林理论浅析[J].集成技术, 2013, 2(1):1-7. DOI: 1

Zhang H R, Min F. Three-way recommender systems based on random forests[J]. Knowledge-Based Systems, 2016, 91(C):275-286. DOI: 275

苏静, 赵毅强, 何家骥, 等.旁路信号主成分分析的欧式距离硬件木马检测[J].微电子学与计算机, 2015, 32(1):1-4. DOI: 1

李欣海.随机森林模型在分类与回归分析中的应用[J].应用昆虫学报, 2013, 50(4):1190-1197. DOI: 1190

Ristin M, Guillaumin M, Gall J, et al. Incremental Learning of Random Forests for Large-Scale Image Classification[J]. IEEE Transactions on Pattern Analysis % Machine Intelligence, 2016, 38(3):490-503. DOI: 490

姚登举, 杨静, 詹晓娟.基于随机森林的特征选择算法[J].吉林大学学报(工学版), 2014, 44(1):137-141. DOI: 137

张华伟, 王明文, 甘丽新.基于随机森林的文本分类模型研究[J].山东大学学报(理学版), 2006, 41(3):5-9. DOI: 5

林海明, 杜子芳.主成分分析综合评价应该注意的问题[J].统计研究, 2013, 30(8):25-31. DOI: 25

陈华锋, 瞿有甜, 姜燕冰.硬件木马电路检测技术及发展趋势[J].浙江大学学报(理学版), 2014, 41(1):49-51. DOI: 49

Xiao K, Forte D, Jin Y, et al. Hardware Trojans: Lessons Learned after One Decade of Research[J]. Acm Transactions on Design Automation of Electronic Systems, 2016, 22(1):6:1-23. DOI: 6:1

王文波, 张晓东, 汪祥莉.基于主成分分析的经验模态分解消噪方法[J].电子学报, 2013, 41(7):1425-1430. DOI: 1425

王力纬, 贾鲲鹏, 方文啸, 等.基于马氏距离的硬件木马检测方法[J].微电子学, 2013, 43(6):817-820. DOI: 817

Xu J, Ramos S. Hierarchical Adaptive Structural SVM for Domain Adaptation[J]. International Journal of Computer Vision, 2016, 119(2):1-20. DOI: 1

倪林, 李少青, 马瑞聪, 等.硬件木马检测与防护[J].数字通信, 2014, 41(1):59-63. DOI: 59

赵志勋, 倪林, 李少青.硬件木马电路功耗的检测方法[J].北京邮电大学学报, 2015, 38(4):128-132. DOI: 128

糜旗, 胡麒, 徐超, 等.硬件木马的分析与检测[J].计算机与现代化, 2012(11):155-157. DOI: 155

薛明富, 胡爱群, 刘威, 等.基于子空间域特征提取的硬件木马检测方法[J].东南大学学报(自然科学版), 2014, 44(3):457-461. DOI: 457

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Adaptive Semi-Supervised Random Forest Algorithm Based on Data Similarity

Lightweight countermeasures against Spectre attacks on the branch prediction unit

A high-efficiency charge pump for ultra-low voltage boost systems

A feature-oriented context understanding and slice analysis network for tuberculosis detection

Research on assembly action recognition based on MSS-YOLO-MPHands fusion architecture

Related Author

HU Zhi-peng

Peng Yi-gong

Related Institution

College of Information Science and Engineering, East China University of Science and Technology ShangHai

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰