南京大学学报(自然科学版) ›› 2020, Vol. 56 ›› Issue (2): 264–269.doi: 10.13232/j.cnki.jnju.2020.02.012

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基于高斯滤波和K最邻近算法融合的硬件木马电磁信息检测技术研究

王品,赵毅强(),刘燕江,何家骥,马浩诚   

  1. 天津大学微电子学院，天津，300072
  • 收稿日期:2019-09-10 出版日期:2020-03-30 发布日期:2020-04-02
  • 通讯作者: 赵毅强 E-mail:yq_zhao@tju.edu.cn
  • 基金资助:
    国家自然科学基金重点基金(61832018)

Hardware Trojan electromagnetic information detection based on fusion of Gaussian filter and K⁃nearest neighbour algorithm

Pin Wang,Yiqiang Zhao(),Yanjiang Liu,Jiaji He,Haocheng Ma   

  1. School of Microelectronics，Tianjin University，Tianjin，300072，China
  • Received:2019-09-10 Online:2020-03-30 Published:2020-04-02
  • Contact: Yiqiang Zhao E-mail:yq_zhao@tju.edu.cn

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摘要：

电磁侧信道信息具有非接触、三维矢量、空间及频谱信息丰富等优点，可以进一步提高硬件木马的检测效率，基于电磁侧信道分析的硬件木马检测技术逐渐成为主流方法.因此，以电磁侧信道信息为研究对象，融合高斯滤波算法和K最邻近算法提取并识别出硬件木马的微小特征，建立高精度微米级集成电路电磁侧信道采集平台，并采集敏感区域的电磁侧信道信息.利用高斯算法自适应地滤除测试中的高斯噪声影响，借助K最邻近算法的相似度测度来提取硬件木马的特征.实验结果表明，提出的检测方法可以有效地检测出面积占比为0.76%的硬件木马.

关键词: 集成电路, 硬件木马, 电磁信息, 高斯滤波, K最邻近算法

Abstract:

Electromagnetic information has many advantages over others，such as non?contact，three?dimensional vector，rich in spatial and frequency information，etc. It could improve the detection efficiency of hardware Trojan，and the detection technology based on electromagnetic information is becoming a main method. Therefore，this paper takes the electromagnetic side?channel signal as the research object，combines the Gaussian filtering algorithm with the K?nearest neighbor algorithm to distinguish the tiny features of the hardware Trojan. The high?precision micro?scale electromagnetic information acquisition platform is established，then applied to collecting the electromagnetic information of the sensitive area. Gaussian algorithm is used to filter out the Gaussian noise adaptively，and the similarity measure of the K?nearest neighbor algorithm is used to extract the characteristics of the hardware Trojan. The experimental results show that the proposed detection method could effectively detect hardware Trojan with an area ratio of 0.76%.

Key words: integrated circuit, hardware Trojan, electromagnetic information, Gaussian filter, K?nearest neighbour algorithm

中图分类号: 

  • TP274

图1

硬件木马检测平台框架"

图2

硬件木马检测流程"

图3

数据降噪处理"

图4

硬件木马检测结果：K最邻近算法（左）；主成分分析算法（右）"

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