期刊名称：International Journal of Computer Network and Information Security
印刷版ISSN：2074-9090
电子版ISSN：2231-4946
出版年度：2021
卷号：13
期号：2
页码：39-52
DOI：10.5815/ijcnis.2021.02.04
出版社：MECS Publisher
摘要：Emergency Message broadcasting is an important process in VANET. Security and reliable transmission are the two major concerns in message broadcasting. VANET is open to unauthorized nodes, hackers, misbehaving vehicles, malicious attackers etc without security. Without valid confirmation of authorized vehicles, these types of attacks may occur. To enhance the reliability in message broadcasting, some existing techniques are used. They transmit the data without much delay but they didn’t provide any trusted authentication. So hackers, malicious nodes, unauthorized vehicles may easily interrupt the emergency messages. Also Brute force attack, Man in Middle attack are not identified and eliminated. In this research, a trust based secured broadcasting mechanism is developed which ensures the metrics such as security, privacy, integrity, trust etc. The major intension of this research is to reduce latency and provide high reliable, secure and efficient communication over the vehicles. The data such as vehicle position, location, speed, and other traffic information’s are generated and stored in a separate table. A network is created with varying densities. A path is generated for message broadcasting between source and destination based on the specific gateway estimated. Here, Optimal Wireless Access in Vanet (OWAV) Protocol is employed to gather vehicle related information to reduce the delay. Blowfish encryption algorithm along with Oppositional Deer Hunting Optimization (ODHO) is used to store the trusted vehicles location to avoid unauthorized tracking. The performance of the proposed research is analyzed with various metrics such as Packet delivery ratio (PDR), transmission delay, encryption time, throughput, computational overhead etc. The efficiency of the research is compared with other existing methods.
关键词：VANET;Security;Active and Passive attacks;Deer Hunting Optimization (DHO);Oppositional behaviour learning (OBL)
