Behavioral Feature and Correlative Detection of Multiple Types of Node in the Internet of Vehicles

doi:10.32604/cmc.2020.09695

	Behavioral Feature and Correlative Detection of Multiple Types of Node in the Internet of Vehicles
	Xie, Pengshou 1; Ma, Guoqiang 1; Feng, Tao 1; Yan, Yan 1,2; Han, Xueming 1
刊名	CMC-COMPUTERS MATERIALS & CONTINUA
	2020
卷号	64 期号:2 页码:1127-1137
关键词	IoV behavioral feature double layer detection feature correlation analysis correlative detection model
ISSN号	1546-2218
DOI	10.32604/cmc.2020.09695
英文摘要	Undoubtedly, uncooperative or malicious nodes threaten the safety of Internet of Vehicles (IoV) by destroying routing or data. To this end, some researchers have designed some node detection mechanisms and trust calculating algorithms based on some different feature parameters of IoV such as communication, data, energy, etc., to detect and evaluate vehicle nodes. However, it is difficult to effectively assess the trust level of a vehicle node only by message forwarding, data consistency, and energy sufficiency. In order to resolve these problems, a novel mechanism and a new trust calculating model is proposed in this paper. First, the four tuple method is adopted, to qualitatively describing various types of nodes of IoV; Second, analyzing the behavioral features and correlation of various nodes based on route forwarding rate, data forwarding rate and physical location; third, designing double layer detection feature parameters with the ability to detect uncooperative nodes and malicious nodes; fourth, establishing a node correlative detection model with a double layer structure by combining the network layer and the perception layer. Accordingly, we conducted simulation experiments to verify the accuracy and time of this detection method under different speed-rate topological conditions of IoV. The results show that comparing with methods which only considers energy or communication parameters, the method proposed in this paper has obvious advantages in the detection of uncooperative and malicious nodes of IoV; especially, with the double detection feature parameters and node correlative detection model combined, detection accuracy is effectively improved, and the calculation time of node detection is largely reduced.
WOS研究方向	Computer Science ; Materials Science
语种	英语
出版者	TECH SCIENCE PRESS
WOS记录号	WOS:000540749400026
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/155268]
专题	计算机与通信学院
作者单位	1.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China; 2.Macquarie Univ, Fac Sci & Engn, Dept Comp, Sydney, NSW 2109, Australia
推荐引用方式 GB/T 7714	Xie, Pengshou,Ma, Guoqiang,Feng, Tao,et al. Behavioral Feature and Correlative Detection of Multiple Types of Node in the Internet of Vehicles[J]. CMC-COMPUTERS MATERIALS & CONTINUA,2020,64(2):1127-1137.
APA	Xie, Pengshou,Ma, Guoqiang,Feng, Tao,Yan, Yan,&Han, Xueming.(2020).Behavioral Feature and Correlative Detection of Multiple Types of Node in the Internet of Vehicles.CMC-COMPUTERS MATERIALS & CONTINUA,64(2),1127-1137.
MLA	Xie, Pengshou,et al."Behavioral Feature and Correlative Detection of Multiple Types of Node in the Internet of Vehicles".CMC-COMPUTERS MATERIALS & CONTINUA 64.2(2020):1127-1137.