Sybil Misbehavior Detection in Software Defined VANETs using Received Signal Strength

Abstract

Software-defined Vehicular ad hoc Networks (SDVN) are the new research area where vehicular networks use the SDN technology for efficient network management. In SDN the network is divided into control plane and data plane by which the load of the network is efficiently managed. The control plane is responsible for building the rules and the data plane is responsible for transferring data between the devices or nodes. As important information such as safety-related, and non-safety related are exchanged between the vehicles, the nodes in the network should be legitimate. SDVN is mostly affected by many attackers in the network which disrupts the whole performance. One such most dangerous attack is Sybil attack where the attacker creates fake identities in the network to take control of the network. This attack should be detected well else the network performance decreases. In this paper, a Sybil misbehavior detection approach is proposed for SDVN using the Received Signal Strength (RSS) of a vehicle. The RSS values are computed by On Board Unit (OBU) when the beacons are regularly received from the neighboring vehicles. Then the vehicles with the same RSS values are grouped to check the nodes as Sybil nodes by finding the distances of the neighbor vehicles. The simulation is performed using OMNeT++ simulator and SUMO road traffic simulator. The method is compared with competing schemes. From the results, it is observed that the proposed approach performs better in terms of detection accuracy, false-positive rate, false-negative rate, and detection time.