Analysis of Internet of Things Intrusion Detection Technology Based on Deep Learning  

作　　者：Huijuan Zheng Yongzhou Wang 

机构地区：[1]Chongqing University of Mobile Communication,Chongqing 401420,China

出　　处：《Journal of Electronic Research and Application》2025年第2期233-239,共7页电子研究与应用

基　　金：the research result of the 2022 Municipal Education Commission Science and Technology Research Plan Project“Research on the Technology of Detecting Double-Surface Cracks in Concrete Lining of Highway Tunnels Based on Image Blast”(KJQN02202403);the first batch of school-level classroom teaching reform projects“Principles Applications of Embedded Systems”(23JG2166);the school-level reform research project“Continuous Results-Oriented Practice Research Based on BOPPPS Teaching Model-Taking the‘Programming Fundamentals’Course as an Example”(22JG332).

摘　　要：With the rapid development of modern information technology,the Internet of Things(IoT)has been integrated into various fields such as social life,industrial production,education,and medical care.Through the connection of various physical devices,sensors,and machines,it realizes information intercommunication and remote control among devices,significantly enhancing the convenience and efficiency of work and life.However,the rapid development of the IoT has also brought serious security problems.IoT devices have limited resources and a complex network environment,making them one of the important targets of network intrusion attacks.Therefore,from the perspective of deep learning,this paper deeply analyzes the characteristics and key points of IoT intrusion detection,summarizes the application advantages of deep learning in IoT intrusion detection,and proposes application strategies of typical deep learning models in IoT intrusion detection so as to improve the security of the IoT architecture and guarantee people’s convenient lives.

关 键 词：Deep learning Internet of Things Intrusion detection technology 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]