北京航空航天大学学报 ›› 2021, Vol. 47 ›› Issue (11): 2200-2207.doi: 10.13700/j.bh.1001-5965.2020.0420

• 论文 • 上一篇    下一篇

一种基于深度学习的电磁信息泄漏检测方法

茅剑1, 刘泰康2, 刘培国3   

  1. 1. 集美大学 计算机工程学院, 厦门 361021;
    2. 中国电子科技集团有限公司 第三十三研究所, 太原 20043;
    3. 国防科学技术大学 电子科学学院, 长沙 410073
  • 收稿日期:2020-08-12 发布日期:2021-12-04
  • 通讯作者: 茅剑 E-mail:maojian@jmu.edu.cn
  • 基金资助:
    国家自然科学基金(61672335)

An electromagnetic information leakage detection method using deep learning

MAO Jian1, LIU Taikang2, LIU Peiguo3   

  1. 1. College of Computer Engineering, Jimei University, Xiamen 361021, China;
    2. No. 33 Institute, China Electronics Technology Group Corporation, Taiyuan 200433, China;
    3. College of Electronic Science, National University of Defense Technology, Changsha 410073, China
  • Received:2020-08-12 Published:2021-12-04
  • Supported by:
    National Natural Science Foundation of China (61672335)

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。

关键词: 信息安全, 深度学习, 信号检测, 无意电磁发射, 电磁信息泄漏

Abstract: Electronic information equipment will emit electromagnetic wave unintentionally, which contains useful information. It will lead to the electromagnetic information leakage, thus threatening the information security. The traditional electromagnetic information leakage detection methods are difficult to extract useful information from uncertain electromagnetic leakage signals in complex environments. Aimed at the problem of electromagnetic information security, the electromagnetic information leakage detection is studied. A detection method based on deep learning is proposed. The method designs a one-dimensional convolutional neural network suitable for electromagnetic signals, and combines an improved gradient-weighted class activation mapping algorithm. It can locate and extract the electromagnetic leakage information characteristics intelligently under the condition of unknown the characteristics through deep learning so as to solve the problem of extracting electromagnetic leakage information in complex environments. The effectiveness of the proposed method is verified by experiments and simulation.

Key words: information security, deep learning, signal detection, unintentional electromagnetic emission, electromagnetic information leakage

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