The 5G and satellite converged communication network(5G SCCN)is an impor⁃tant component of the integration of satellite-terrestrial networks,the national science,and technology major projects towards 2030.Security is ...The 5G and satellite converged communication network(5G SCCN)is an impor⁃tant component of the integration of satellite-terrestrial networks,the national science,and technology major projects towards 2030.Security is the key to ensuring its operation,but at present,the research in this area has just started in our country.Based on the network char⁃acteristics and security risks,we propose the security architecture of the 5G SCCN and sys⁃tematically sort out the key protection technologies and improvement directions.In particu⁃lar,unique thinking on the security of lightweight data communication and design reference for the 5G SCCN network architecture is presented.It is expected to provide a piece of refer⁃ence for the follow-up 5G SCCN security technology research,standard evolution,and indus⁃trialization.展开更多
User Equipment(UE)authentication holds paramount importance in upholding the security of wireless networks.A nascent technology,Radio Frequency Fingerprint Identification(RFFI),is gaining prominence as a means to bols...User Equipment(UE)authentication holds paramount importance in upholding the security of wireless networks.A nascent technology,Radio Frequency Fingerprint Identification(RFFI),is gaining prominence as a means to bolster network security authentication.To expedite the integration of RFFI within fifth-generation(5G)networks,this research undertakes the creation of a comprehensive link-level simulation platform tailored for 5G scenarios.The devised platform emulates various device impairments,including an oscillator,IQ modulator,and power amplifier(PA)nonlinearities,alongside simulating channel distortions.Consequent to this,a plausibility analysis is executed,intertwining transmitter device impairments with 3rd Generation Partnership Project(3GPP)new radio(NR)protocols.Subsequently,an exhaustive exploration is conducted to assess the impact of transmitter impairments,deep neural networks(DNNs),and channel effects on RF fingerprinting performance.Notably,under a signal-to-noise ratio(SNR)of 15 d B,the deep learning approach demonstrates the capability to accurately classify 100 UEs with a commendable 91%accuracy rate.Through a multifaceted evaluation,it is ascertained that the Attention-based network architecture emerges as the optimal choice for the RFFI task,serving as the new benchmark model for RFFI applications.展开更多
文摘The 5G and satellite converged communication network(5G SCCN)is an impor⁃tant component of the integration of satellite-terrestrial networks,the national science,and technology major projects towards 2030.Security is the key to ensuring its operation,but at present,the research in this area has just started in our country.Based on the network char⁃acteristics and security risks,we propose the security architecture of the 5G SCCN and sys⁃tematically sort out the key protection technologies and improvement directions.In particu⁃lar,unique thinking on the security of lightweight data communication and design reference for the 5G SCCN network architecture is presented.It is expected to provide a piece of refer⁃ence for the follow-up 5G SCCN security technology research,standard evolution,and indus⁃trialization.
基金supported by the National Natural Science Foundation of China(No:62201172)the National Key Research and Development Program of China(2022YFE0136800)
文摘User Equipment(UE)authentication holds paramount importance in upholding the security of wireless networks.A nascent technology,Radio Frequency Fingerprint Identification(RFFI),is gaining prominence as a means to bolster network security authentication.To expedite the integration of RFFI within fifth-generation(5G)networks,this research undertakes the creation of a comprehensive link-level simulation platform tailored for 5G scenarios.The devised platform emulates various device impairments,including an oscillator,IQ modulator,and power amplifier(PA)nonlinearities,alongside simulating channel distortions.Consequent to this,a plausibility analysis is executed,intertwining transmitter device impairments with 3rd Generation Partnership Project(3GPP)new radio(NR)protocols.Subsequently,an exhaustive exploration is conducted to assess the impact of transmitter impairments,deep neural networks(DNNs),and channel effects on RF fingerprinting performance.Notably,under a signal-to-noise ratio(SNR)of 15 d B,the deep learning approach demonstrates the capability to accurately classify 100 UEs with a commendable 91%accuracy rate.Through a multifaceted evaluation,it is ascertained that the Attention-based network architecture emerges as the optimal choice for the RFFI task,serving as the new benchmark model for RFFI applications.
