An authentication and plausibility model for big data analytic under LOS and NLOS conditions in 5G-VANET
An authentication and plausibility model for big data analytic under LOS and NLOS conditions in 5G-VANET作者机构：School of Computing Faculty of Engineering Universiti Teknologi Malaysia (UTM) School of Computer Science and Electronic Engineering University of Essex Centre of Artificial Intelligence National University of Malaysia (UKM) Center of Excellence in Information Assurance (CoEIA) King Saud University Advanced Informatics School Menara Razak Universiti Teknologi Malaysia (UTM)
出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))
年 卷 期：2020年第63卷第12期
页 面：67-83页
核心收录：
学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 082304[工学-载运工具运用工程] 08[工学] 080402[工学-测试计量技术及仪器] 080204[工学-车辆工程] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 0802[工学-机械工程] 0823[工学-交通运输工程]
基 金：supported by Ministry of Education, Malaysia, in collaboration with the Research Management Center, Universiti Teknologi Malaysia (Grant No. Q.J130000.2451.04G80) Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (Grant No. GGPM-2020-029) partially supported by King Saud University (Grant No. RSP-2019/12), Riyadh, Saudi Arabia
主 题：authentication plausibility fuzzy logic cuckoo filter 5G-VANET big data
摘 要：The exchange of correct and reliable data among legitimate nodes is one of the most important challenges in vehicular ad hoc networks(VANETs). Malicious nodes and obstacles, by generating inaccurate information, have a negative impact on the security of 5G-VANET. The big data generated in the vehicular network is also an issue in the security of VANET. To this end, a security model based on authentication and plausibility is proposed to improve the safety of network named ‘AFPM’. In the first layer, an authentication mechanism using edge nodes along with 5G is proposed to deal with the illegitimate nodes who enter the network and broadcast wrong information. In the authentication mechanism, because of the growth of the connected vehicles to the edge nodes that lead to generating big data and hence the inappropriateness of the traditional data structures, cuckoo filter, as a space-efficient probabilistic data structure, is used. In the second layer, a plausibility model by performing fuzzy logic is presented to cope with inaccurate information. The plausibility model is based on detection of inconsistent data involved in the event message. The plausibility model not only tackles with inaccurate, incomplete, and inaccuracy data but also deals with misbehaviour nodes under both line-of-sight(LOS) and non-line-of-sight(NLOS) conditions. All obtained results are validated through well-known evaluation measures such as F-measure and communication overhead. The results presented in this paper demonstrate that the proposed security model possesses a better performance in comparison with the existing studies.
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