显示器电磁信息泄漏的机器学习检测方法研究

Research on the detection method of electromagnetic information leakage from display by machine learning

根据电磁学原理,在操作电子信息设备的过程中会产生无意的电磁辐射。电磁辐射会引发信息泄漏,给信息安全造成严重威胁。面向计算机显示器的电磁信息安全问题,提出基于机器学习的电磁信息泄漏检测方法。针对电磁泄漏信号的特点,设计了MGCNN卷积神经网络。利用其独特的卷积和池化处理能力,提取显示器电磁频谱信号中图像信息的多层次特征,克服了传统检测方法需要事前明确电磁信息特征和缺乏自适应能力的缺陷,从而有效地解决电磁信号中的信息泄漏检测问题。通过实测对比,证明了MGCNN对于显示器的电磁信息泄漏检测的有效性。

According to the principles of electromagnetics,unintentional electromagnetic radiation is generated during the operation of electronic information equipment.Electromagnetic radiation can cause information leakage and pose a serious threat to information security.Faced with the problem of electromagnetic information security of computer monitors,a method of electromagnetic information leakage detection based on machine learning is proposed.According to the characteristics of electromagnetic leakage signal,MGCNN convolutional neural network is designed.Using its unique convolution and pooling processing capabilities,MGCNN extracts multi-level features of image information in the electromagnetic spectrum signal of the display.It overcomes the defects of traditional detection methods that need to make clear the characteristics of electromagnetic information in advance and lack of adaptive ability,so as to effectively solve the problem of information leakage detection in electromagnetic signals.The effectiveness of MGCNN in detecting the electromagnetic information leakage of the display is proved through the actual measurement and comparison.