Malicious node detection method based on clustering trust evaluation in internet of vehicles

Abstract

The invention relates to the field of network communication safety of the internet of vehicles, in particular to a malicious node detection method based on clustering trust evaluation in the internet of vehicles. The malicious node detection method comprises the following steps of clustering and cluster head selection; cluster head node trust evaluation; trust evaluation on member nodes in clusters; cluster trust evaluation and node trust update. A network is divided into a plurality of clusters, intra-cluster nodes are communicated, and different clusters are communicated through cluster heads. The method can adapt to a large VANET network and is good in extendibility, and the communication cost of different intra-cluster member nodes is reduced. In addition, a recommendation chain on the aspect of recommendation trust computation is simplified, and the computation is performed by directly using packet loss rate.

Description

Technical field [0001] The invention relates to the field of vehicle-linked network communication security, in particular to a malicious node detection method based on cluster trust evaluation in the vehicle-linked network. Background technique [0002] The Internet of Vehicles is a vehicle-mounted self-organizing network. It is a fast-moving broadband multi-hop wireless network used to achieve communication between vehicles and between vehicles and roadside infrastructure during the movement, so that vehicles within a certain range can communicate with each other. Exchanging each other's status information and road traffic information can not only improve traffic efficiency, but also ensure the safety of drivers. However, the openness, rapid topology change, and high degree of autonomy of the Internet of Vehicles network make it face more severe security challenges than ordinary mobile ad hoc networks, such as malicious vehicles spreading false road information, and selfish vehi...

AI Technical Summary

Problems solved by technology

In this model method, the trust degree assigned by the source node to the destination node is calculated according to the trust degree of the intermediate node. When there are multiple intermediate nodes, the entire recommendation chain is relatively long and the calculation is more complicated.

In this method, all vehicle nodes are at the same level and lack unified management. When the network scale is large and the number of vehicle nodes is large, the control and routing overhead is large and the scalability is poor, and the relationship of trust over time is not considered. Therefore, it is impossible to detect the situation that the original normal node becomes a malicious node

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