典型文献
Moving target defense of routing randomization with deep reinforcement learning against eavesdropping attack
文献摘要：
Eavesdropping attacks have become one of the most common attacks on networks because of their easy imple-mentation.Eavesdropping attacks not only lead to transmission data leakage but also develop into other more harmful attacks.Routing randomization is a relevant research direction for moving target defense,which has been proven to be an effective method to resist eavesdropping attacks.To counter eavesdropping attacks,in this study,we analyzed the existing routing randomization methods and found that their security and usability need to be further improved.According to the characteristics of eavesdropping attacks,which are"latent and transferable",a routing randomization defense method based on deep reinforcement learning is proposed.The proposed method realizes routing randomization on packet-level granularity using programmable switches.To improve the security and quality of service of legitimate services in networks,we use the deep deterministic policy gradient to generate random routing schemes with support from powerful network state awareness.In-band network telemetry pro-vides real-time,accurate,and comprehensive network state awareness for the proposed method.Various exper-iments show that compared with other typical routing randomization defense methods,the proposed method has obvious advantages in security and usability against eavesdropping attacks.
文献关键词：
中图分类号：
作者姓名：
Xiaoyu Xu;Hao Hu;Yuling Liu;Jinglei Tan;Hongqi Zhang;Haotian Song
作者机构：
Academy of People's Armed Police,Beijing,100012,China;PLA SSF Information Engineering University,Zhengzhou,450001,China;Institute of Information Engineering,Chinese Academy of Sciences,Beijing,100093,China;School of Cyber Security,University of Chinese Academy of Sciences,Beijing,101408,China
文献出处：
引用格式：
[1]Xiaoyu Xu;Hao Hu;Yuling Liu;Jinglei Tan;Hongqi Zhang;Haotian Song-.Moving target defense of routing randomization with deep reinforcement learning against eavesdropping attack)[J].数字通信与网络（英文）,2022(03):373-387
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B类：
Moving,target,defense,routing,randomization,deep,reinforcement,learning,against,eavesdropping,Eavesdropping,attacks,have,become,one,most,common,networks,because,their,easy,imple,mentation,not,only,lead,transmission,data,leakage,but,also,develop,into,other,more,harmful,Routing,relevant,research,direction,moving,which,has,been,proven,effective,resist,To,counter,this,study,analyzed,existing,methods,found,that,security,usability,need,further,improved,According,characteristics,latent,transferable,proposed,realizes,packet,level,granularity,using,programmable,switches,quality,legitimate,services,deterministic,policy,gradient,generate,schemes,support,from,powerful,state,awareness,In,band,telemetry,vides,accurate,comprehensive,Various,exper,iments,show,compared,typical,obvious,advantages
AB值：
0.527304
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