基于遗传算法的硬件木马检测方法

刘燕江; 何春华; 王力纬; 恩云飞; 谢少锋; 谢云

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基于遗传算法的硬件木马检测方法

更新时间：2026-09-04

- 基于遗传算法的硬件木马检测方法
- A Novel Hardware Trojan Detection Method Based on Genetic Algorithm
- 微电子学与计算机 2016年33卷第11期页码：74-77
- 作者机构：
  
  1. 工业和信息化部电子第五研究所电子元器件可靠性物理及其应用技术重点实验室,广东,广州,510610
  2. 广东工业大学机构自动化学院,广东,广州,510000
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- 纸质出版：2016
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刘燕江, 何春华, 王力纬, 恩云飞, 谢少锋, 谢云. 基于遗传算法的硬件木马检测方法[J]. 微电子学与计算机, 2016, 33(11): 74-77.

LIU Yan-jiang, HE Chun-hua, WANG Li-wei, et al. A Novel Hardware Trojan Detection Method Based on Genetic Algorithm[J]. 2016, 33(11): 74-77.
刘燕江, 何春华, 王力纬, 恩云飞, 谢少锋, 谢云. 基于遗传算法的硬件木马检测方法[J]. 微电子学与计算机, 2016, 33(11): 74-77. DOI：

LIU Yan-jiang, HE Chun-hua, WANG Li-wei, et al. A Novel Hardware Trojan Detection Method Based on Genetic Algorithm[J]. 2016, 33(11): 74-77. DOI：

摘要

针对硬件木马严重威胁到芯片的安全性和系统的可靠性的问题

提出了一种基于遗传算法的集成电路硬件木马检测方法

该方法将K均值算法的局部收敛能力和遗传算法的全局收敛能力结合起来

提取旁路信息间的微小特征差异

实现硬件木马的在线自动检测.本实验在FPGA芯片实现硬件验证

以全局时钟、动态功耗和环形振荡器的输出三个信号作为研究对象

搭建集成电路硬件木马检测系统采集三维旁路信息

对160个样本芯片数据进行聚类分析

实验结果证明该方法可准确有效地检测出硬件木马

检测分辨率达到10量级.

Abstract

Hardware Trojan

a malicious circuit inserted into the golden circuit

causes a serious risk to the security of Integrated Circuits and the trust of critical system. Thus

hardware Trojan detection is of great significance. A novel hardware Trojan detection method based on clustering analysis algorithm is presented in this work

which employs the global optimized ability of genetic algorithm and the local searching capability of K-means algorithm to automatically fulfill the detection online. Side-channel signals

such as global clock signal

reset signal and a ring oscillator signal of a chip

are applied as the inputs of the clustering algorithm

since they may be altered by the Trojan circuit. Experimental results implemented with FPGAs demonstrate that the tested chips can be clustered into two categories

and the Trojan chips can be distinguished from the Golden chips accurately. The detection resolution achieves about 10

which indicates the proposed novel detection method is feasible and effective.

关键词

Keywords

references

Cambridge Holland J H. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence[M]2nd ed. Cambridge: MIT Press, 1992.

杨淑莹. 模式识别与智能计算-Matlab技术实现[M]. 第2版. 北京: 电子工业出版社, 2011.

Shila D M, Venugopal V. Design, implementation and security analysis of Hardware Trojan Threats in FPGA[C]//Proc of IEEE Communication and Information Systems Security Symposium. New York, NY, USA: ACM, 2014: 247-24.

Kitsos P, Voyiatzis A G. FPGA trojan detection using length-optimized ring oscillators[C]// Proc of 17th Euromicro Conference on Digital System Design. Paris kitsos: IEEE, 2014: 675-678.

Xiao K, Forte D, Tehranipoor M. A Novel Built-In Self-Authentication Technique to Prevent Inserting Hardware Trojans[J]. IEEE Transactions on computer-aided design of integrated circuits and systems, 2014, 33(12): 1778-1791. DOI: 1778

张鹏, 王新成, 周庆. 基于电磁辐射信号分析的芯片硬件木马检测[J]. 电子学报, 2014, 42(2): 341-346. DOI: 341

Bhunia S, Hsiao M S, Banga M, et al. Hardware Trojan Attacks: Threat Analysis and Countermeasures[J]. Proceedings of the IEEE, 2014, 10(8): 1229-1247. DOI: 1229

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

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