节点文献

基于机器学习的通信网络涉密信息感知系统设计

Design of communication network secret-involved information perception system based on machine learning

  • 推荐 CAJ下载
  • PDF下载
  • 不支持迅雷等下载工具,请取消加速工具后下载。

【作者】 冯萍孙伟李国梁

【Author】 FENG Ping;SUN Wei;LI Guoliang;College of Computer Science and Technology,Jilin University;College of Computer Science and Technology,Changchun University;Graduate School,Changchun University;College of Network Security,Changchun University;

【机构】 吉林大学计算机科学与技术学院长春大学计算机科学与技术学院长春大学研究生学院长春大学网络安全学院

【摘要】 现有的涉密信息感知系统信息处理能力较差,对涉密信息的感知能力难以达到用户要求。为此,基于机器学习技术设计一种新的通信网络涉密信息感知系统。在机器学习技术下,系统的硬件部分主要由信息存储模块、液晶显示器模块和感知系统串口模块组成,然后通过信息采集、信息处理、信息存储和信息感知过程实现对系统软件流程的设计。为验证该系统的应用效果,在实验部分将其与现有的涉密信息感知系统进行性能对比,结果表明,基于机器学习的通信网络涉密信息感知系统有较强的信息处理能力和对涉密信息的感知能力。

【Abstract】 The information processing abilities of the existing secret-involved information perception systems are poor.Moreover,their perception abilities for the secret-involved information are far away from meeting the requirements of users. For this reason,a new communication network secret-involved information perception system based on machine learning technology is designed. Under the background of the machine learning technology,the system hardware is composed of the information storage module,liquid crystal display(LCD) module and serial port module. The software flow of the system is achieved by means of the information acquisition,information processing,information storage and information perception progress. An experiment was performed to compare the performance of the proposed system with those of the existing secret-involved information perception systems to verify the application effect of the proposed system. The results show that the communication network secret-involved information perception system based on the machine learning has strong information processing ability and secret-involved information perception ability.

【基金】 吉林省教育厅“十三五”科学研究规划项目:基于视触觉增强现实技术的脑卒中个性化训练系统关键技术研究(2020LY530L44)
  • 【文献出处】 现代电子技术 ,Modern Electronics Technique , 编辑部邮箱 ,2021年19期
  • 【分类号】TP181;TN918
  • 【被引频次】1
  • 【下载频次】303
节点文献中: 

本文链接的文献网络图示:

本文的引文网络