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一种基于深度学习的电磁信息泄漏检测方法

茅剑 刘泰康 刘培国

茅剑, 刘泰康, 刘培国等 . 一种基于深度学习的电磁信息泄漏检测方法[J]. 北京航空航天大学学报, 2021, 47(11): 2200-2207. doi: 10.13700/j.bh.1001-5965.2020.0420
引用本文: 茅剑, 刘泰康, 刘培国等 . 一种基于深度学习的电磁信息泄漏检测方法[J]. 北京航空航天大学学报, 2021, 47(11): 2200-2207. doi: 10.13700/j.bh.1001-5965.2020.0420
MAO Jian, LIU Taikang, LIU Peiguoet al. An electromagnetic information leakage detection method using deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(11): 2200-2207. doi: 10.13700/j.bh.1001-5965.2020.0420(in Chinese)
Citation: MAO Jian, LIU Taikang, LIU Peiguoet al. An electromagnetic information leakage detection method using deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(11): 2200-2207. doi: 10.13700/j.bh.1001-5965.2020.0420(in Chinese)

一种基于深度学习的电磁信息泄漏检测方法

doi: 10.13700/j.bh.1001-5965.2020.0420
基金项目: 

国家自然科学基金 61672335

详细信息
    通讯作者:

    茅剑, E-mail: maojian@jmu.edu.cn

  • 中图分类号: TP309.1;TN911.23

An electromagnetic information leakage detection method using deep learning

Funds: 

National Natural Science Foundation of China 61672335

More Information
  • 摘要:

    电子信息设备工作时无意发射的电磁波中包含有用信息,会导致电磁信息泄漏,从而威胁设备的信息安全。现有的电磁信息泄漏检测方法,在复杂现场环境下,难以从具有不确定性的电磁泄漏信号中提取有用信息。面向电磁信息安全问题,开展了电磁信息泄漏检测研究,提出了一种基于深度学习的检测方法。设计了一个适用于电磁泄漏信号的一维卷积神经网络,并结合改进的梯度加权类激活映射方法,在未知电磁信息泄漏特征的前提下,通过深度学习实现电磁信息泄漏特征的智能标定和自动提取,从而解决了现场环境下电磁信息泄漏检测难以提取有用信息的问题。分别通过实测和仿真对比实验,验证了所提方法的有效性。

     

  • 图 1  基于深度学习的电磁信息泄漏检测原理

    Figure 1.  Electromagnetic information leakage detection principle based on deep learning

    图 2  EM-CNN处理电磁泄漏信号示意图

    Figure 2.  Schematic diagram of electromagnetic leakage signal processing by EM-CNN

    图 3  计算机显示器电磁信息泄漏标定的实测验证

    Figure 3.  Experimental verification for electromagnetic information leakage detection on computer displayer

    图 4  不同阈值下的仿真性能曲线

    Figure 4.  Simulation performance curves under different thresholds

    图 5  EM-CNN与AlexNet的性能比较

    Figure 5.  Performance comparison between EM-CNN and AlexNet

    图 6  不同CNN的检测准确率比较

    Figure 6.  Comparison of detection accuracy rates using different CNNs

    图 7  不同CNN的检测召回率比较

    Figure 7.  Comparison of detection recall rates using different CNNs

    表  1  EM-CNN结构参数

    Table  1.   Parameters of EM-CNN structure

    神经网络层次 卷积核/滤波器尺寸 计算步长 通道数
    卷积层1 4 2 32
    池化层1 2 2 32
    卷积层2 12 2 32
    池化层2 2 2 32
    卷积层3 12 2 32
    池化层3 2 2 32
    卷积层4 12 2 64
    池化层4 2 2 64
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-08-12
  • 录用日期:  2021-01-08
  • 网络出版日期:  2021-11-20

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