Research On LCD Electromagnetic Information Leakage Identification Method Based On Deep Learning

With the continuous development of the information society,information technology has become a very important technology in today's society.Among them,the problem of information leakage in the electromagnetic field has also become an object of increasing concern.The leakage of electromagnetic information and sensitive information are intercepted to national information security.The threats are gradually increasing,and the demand for electromagnetic information security is also facing an urgent situation.The leakage of electromagnetic information from computer monitors has become an important technical support for information security.This article analyzes the current research status of computer electromagnetic information leakage,taking the electromagnetic signal leakage of computer display equipment as the starting point,combining electromagnetic information leakage feature identification technology,deep learning technology and virtual instrument electromagnetic monitoring and identification system,which is different from the traditional experience-based Artificial feature extraction mode,using artificial intelligence deep learning algorithm,proposed a recognition method based on convolutional neural network,and established a set of monitoring and recognition system applied to complex electromagnetic environment.The main research content and innovation of this paper are summarized as follows:Firstly,from the perspective of electromagnetic information leakage,analyze the working principle of the structure of the computer display system,make a detailed analysis of the computer display system's liquid crystal display and video transmission line,study the video electromagnetic leakage path,and conduct a preliminary positioning and design of the information leakage.Data collection plan to collect electromagnetic information leakage source data.Secondly,for the collected electromagnetic signals,a method is proposed to characterize electromagnetic leakage signals through time-frequency images.The design uses short-time Fourier transform preprocessing to convert onedimensional signals into two-dimensional time-frequency images as network input.Further combining the deep learning convolutional neural network algorithm and the method of electromagnetic signal feature recognition,after research and exploration to build a convolutional neural network structure for electromagnetic information leakage recognition,to achieve a better feature recognition effect,and obtain concrete feature signals.Build a feature recognition model.Finally,based on the software interface design scheme of the virtual instrument platform Lab VIEW,it realizes the control and reception of hardware system transmission data,exchange of instrument parameters,real-time time spectrum diagram,identification of electromagnetic information leakage signals,establishment of a monitoring and identification system,and achieving the effect of monitoring and identifying electromagnetic information leakage.