茅剑,唐妮,刘晋明,等. 计算机线缆的电磁信息泄漏智能分析方法[J]. 电波科学学报,2022,37(4):710-718. DOI: 10.12265/j.cjors.2021196
      引用本文: 茅剑,唐妮,刘晋明,等. 计算机线缆的电磁信息泄漏智能分析方法[J]. 电波科学学报,2022,37(4):710-718. DOI: 10.12265/j.cjors.2021196
      MAO J, TANG N, LIU J M, et al. Intelligent analysis of electromagnetic information leakage from computer cable[J]. Chinese journal of radio science,2022,37(4):710-718. (in Chinese). DOI: 10.12265/j.cjors.2021196
      Citation: MAO J, TANG N, LIU J M, et al. Intelligent analysis of electromagnetic information leakage from computer cable[J]. Chinese journal of radio science,2022,37(4):710-718. (in Chinese). DOI: 10.12265/j.cjors.2021196

      计算机线缆的电磁信息泄漏智能分析方法

      Intelligent analysis of electromagnetic information leakage from computer cable

      • 摘要: 计算机系统中的各型线缆会通过电磁传导发射泄漏内部信息,导致信息安全问题. 为了分析来自计算机线缆的电磁信息泄漏,提出了基于深度学习的智能分析方法. 设计一维卷积神经网络算法,对电磁泄漏信号进行深层的特征提取与学习,从泄漏的电磁信号中智能识别泄漏源的线缆类型,进而分析其中泄漏的视频信息. 实测结果表明,本文提出的方法,在未知目标信号特征的情况下,能够有效识别电磁信息的泄漏源与泄漏信息,为计算机线缆提供了一种电磁信息泄漏的智能分析手段.

         

        Abstract: Electromagnetic(EM) conduction emission through linked cables in computer system can cause internal information leakage, which leads to information security problems. In order to analyze electromagnetic information leakage from computer cables, an intelligent analysis method based on deep learning is proposed. A one-dimensional convolutional neural network algorithm is designed to extract and learn the features of electromagnetic leakage signals, intelligently identify the type of cables that are considered as leakage sources, and then analyze the leaked video information from the leaked electromagnetic signals. The experiment results show that the method presented in this paper effectively detects the source and information of electromagnetic leakage in the case of unknown target signal features. The method provides a way to analyze the electromagnetic information leakage of computer cables intelligently.

         

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