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典型文献
DDoS Detection for 6G Internet of Things:Spatial-Temporal Trust Model and New Architecture
文献摘要:
With the rapid development of the sixth generation(6G)network and Internet of Things(IoT),it has become extremely challenging to efficiently detect and prevent the distributed denial of service(DDoS)attacks originating from IoT devices.In this paper we propose an innovative trust model for IoT de-vices to prevent potential DDoS attacks by evaluating their trustworthiness,which can be deployed in the ac-cess network of 6G IoT.Based on historical communi-cation behaviors,this model combines spatial trust and temporal trust values to comprehensively characterize the normal behavior patterns of IoT devices,thereby effectively distinguishing attack traffic.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Com-pared with the benchmark methods,our method has advantages in terms of both accuracy and efficiency in identifying attack flows.
文献关键词:
作者姓名:
Yinglun Ma;Xu Chen;Wei Feng;Ning Ge
作者机构:
Department of Electronic Engineering,Tsinghua University,Beijing 100084,China;Beijing National Research Center for Information Science and Technology,Beijing 100084,China
引用格式:
[1]Yinglun Ma;Xu Chen;Wei Feng;Ning Ge-.DDoS Detection for 6G Internet of Things:Spatial-Temporal Trust Model and New Architecture)[J].中国通信(英文版),2022(05):141-149
A类:
B类:
DDoS,Detection,6G,Internet,Things,Spatial,Temporal,Trust,Model,New,Architecture,With,rapid,development,sixth,generation,network,IoT,has,become,extremely,challenging,efficiently,detect,prevent,distributed,denial,service,attacks,originating,from,devices,this,paper,we,innovative,model,potential,evaluating,their,trustworthiness,which,can,deployed,cess,Based,historical,communi,cation,behaviors,combines,spatial,temporal,values,comprehensively,characterize,normal,patterns,thereby,effectively,distinguishing,traffic,Experimental,results,show,that,proposed,Com,pared,benchmark,methods,our,advantages,terms,both,accuracy,efficiency,identifying,flows
AB值:
0.594138
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