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 connectio...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.展开更多
With the continuous emergence of cyber-attacks,traditional intrusion detection methods become increasingly limited.In the field of network security,new intrusion detection methods are needed to ensure network security...With the continuous emergence of cyber-attacks,traditional intrusion detection methods become increasingly limited.In the field of network security,new intrusion detection methods are needed to ensure network security.To solve the problems,relevant knowledge involvedwas first introduced.Then,an intrusion detection system based on neural network was designed according to the general intrusion detection framework,and the design of the event collector and analyzer in the systemwas described in detail.Experimentswere conductedwith theweight initialization method of the neural network model,the selection of the activation function,and the selection of the optimizer.Finally,the most suitable hyperparameters were determined and the optimal neural network model was trained.The test results show that the application of neural network to the intrusion detection system can greatly improve the accuracy of intrusion detection,thereby improving the security of computer networks.展开更多
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).
文摘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.
基金National Natural Science Foundation of China(62072416)Henan Province Science and Technology Research Project(202102210176)+2 种基金Zhongyuan Science and Technology Innovation Leader of Zhongyuan Talent Project(214200510026)Henan Province Science and Technology Project(212102210429)The fourth batch of innovative leading talents of Zhihui Zhengzhou 1125 talent gathering plan(ZhengZheng[2019]No.21).
文摘With the continuous emergence of cyber-attacks,traditional intrusion detection methods become increasingly limited.In the field of network security,new intrusion detection methods are needed to ensure network security.To solve the problems,relevant knowledge involvedwas first introduced.Then,an intrusion detection system based on neural network was designed according to the general intrusion detection framework,and the design of the event collector and analyzer in the systemwas described in detail.Experimentswere conductedwith theweight initialization method of the neural network model,the selection of the activation function,and the selection of the optimizer.Finally,the most suitable hyperparameters were determined and the optimal neural network model was trained.The test results show that the application of neural network to the intrusion detection system can greatly improve the accuracy of intrusion detection,thereby improving the security of computer networks.
