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典型文献
Joint Spectrum Sensing and Spectrum Access for Defending Massive SSDF Attacks:A Novel Defense Framework
文献摘要:
Multiple secondary users(SUs)perform collaborative spectrum sensing(CSS)in cognitive radio networks to improve the sensing performance.However,this system severely degrades with spectrum sensing data falsification(SSDF)attacks from a large number of mali-cious secondary users,i.e.,massive SSDF attacks.To mit-igate such attacks,we propose a joint spectrum sensing and spectrum access framework.During spectrum sensing,each SU compares the decisions of CSS and independent spectrum sensing(IndSS),and then the reliable decisions are adopted as its final decisions.Since the transmission slot is divided into several tiny slots,at the stage of spec-trum access,each SU is assigned with a specific tiny time slot.In accordance with its independent final spectrum decisions,each node separately accesses the tiny time slot.Simulation results verify effectiveness of the proposed al-gorithm.
文献关键词:
作者姓名:
XU Zhenyu;SUN Zhiguo;GUO Lili;Muhammad Zahid Hammad;Chintha Tellambura
作者机构:
College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China;Department of Electrical and Computer Engineering,University of Alberta,Edmonton T6G1H9,Canada
引用格式:
[1]XU Zhenyu;SUN Zhiguo;GUO Lili;Muhammad Zahid Hammad;Chintha Tellambura-.Joint Spectrum Sensing and Spectrum Access for Defending Massive SSDF Attacks:A Novel Defense Framework)[J].电子学报(英文),2022(02):240-254
A类:
Defending,SSDF,falsification,cious,IndSS,accesses
B类:
Joint,Spectrum,Sensing,Access,Massive,Attacks,Novel,Defense,Framework,Multiple,secondary,users,SUs,collaborative,spectrum,sensing,CSS,cognitive,radio,networks,improve,performance,However,this,system,severely,degrades,data,attacks,from,large,number,mali,massive,To,mit,igate,such,joint,framework,During,each,compares,decisions,independent,then,reliable,adopted,its,final,Since,transmission,divided,into,several,tiny,slots,stage,assigned,specific,accordance,node,separately,Simulation,results,verify,effectiveness,proposed,gorithm
AB值:
0.496687
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