Movement Abnormality Evaluation Model in the Partially Centralized VANETs for Prevention Against Sybil Attack

Abstract

The VANETs carry many security concerns. One of the popular and dangerous attacks can be launched in the form of Sybil or Prankster attack, where an attacker inserts a fake position within in the cluster. The inserted fake node information can be utilized by the hackers in the case of selfish driver, traffic jams, selective collisions and other similar hazardous situations. To avoid such things the VANETs must be protected against such attacks. In this paper, a novel solution has been proposed to overcome the Sybil and prankster attacks on the VANETs. The new solution is capable of detecting the fake information injections by verifying the VANET node behaviour in the cluster. The behaviour of the node includes the direction, speed, pattern, etc. In case a node is found malicious, the whole cluster is reported against that node, and node is ordered to stop by the central control system. The proposed model has been developed using the random waypoint model. The random way point model is much closer to the real time VANETs. The random waypoint model has been compared against the reference point group model. The experimental results have shown the effectiveness of the proposed model.