A distributed prevention scheme from malicious nodes in VANETs' routing protocols

Vehicular environments are vulnerable to attacks because of the continuous interactions between vehicles despite authentication techniques deployed by communication standards. In fact, an authenticated node with a certificate could initiate an attack while complying with implemented protocols if it has malicious intentions and benefit from this always on connection to threaten the network accuracy. Several mechanisms to counter these attacks were proposed but none of them is able to anticipate the behavior of nodes. In the present work, we target this problem by proposing a preventive mechanism able to predict the behavior of vehicles and prevent from attacks. We use Kalman filter to predict the future behavior of vehicles and classify them into three categories (white, gray and black) based on their expected trustworthiness. The main concern of this work is to prevent from the denial of service (DoS) attack. Results, given by the implementation of the proposed mechanism over an intersection-based routing protocol using ns3 simulator, prove its accuracy regarding the detection rate and a good impact on packets delivery ratio and end-to-end delay.