In recent years, vehicles became able to establish connections with other vehicles and infrastructure units that are located in the roadside. In the near future, the vehicular network will be expanded to include the communication between vehicles and any smart devices in the roadside which is called Vehicle-to-Everything (V2X) communication. The vehicular network causes many challenges due to heterogeneous nodes, various speeds and intermittent connection, where traditional security methods are not always efficacious. As a result, an extensive variety of research works has been done on optimizing security solutions whilst considering network requirements. In this paper, we present a comprehensive survey and taxonomy of the existing security solutions for V2X communication technology. Then, we provide discussions and comparisons with regard to some pertinent criteria. Also, we present a threat analysis for V2X enabling technologies. Finally, we point out the research challenges and some future directions.

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Intelligent Transportation System (ITS) is one of the main systems which have been developed to achieve safe traffic and efficient transportation. It enables the vehicles to establish connections with other road entities and infrastructure units using Vehicle-to-Everything (V2X) communications. As a consequence, all road entities become exposed to either internal or external attacks. Internal attacks cannot be detected by traditional security schemes. In this paper, a recommendationbased trust model for V2X communications is proposed to defend against internal attacks. Four types of malicious attacks are analysed. In addition, we conduct various experiments with different percentage of malicious nodes to measure the performance of the proposed model. In comparison with the existing model, the proposed model shows an improvement in the network throughput and the detection rate for all types of considered malicious behaviors. Our model improves the Packet Dropping Rate (PDR) with 36% when the percentage of malicious nodes is around 87.5%.