Wireless vehicular communication is used to enhance traffic safety and to minimize congestion, thereby leading to increased driving efficiency. A malicious node can transmit an inaccurate message to trigger inevitable situations by pretending to be multiple (other) vehicles. Therefore, it is critical to identify malicious nodes as well as fake messages generated by such nodes, and discard such messages quickly. In a Sybil attack, an attacker participates in the network with multiple forged identities in order to disrupt the fundamental operations of VANET. Sybil attacks are particularly easy to launch in VANETs due to the open and broadcast nature of communication medium. In this paper, we present the implementation of simulated Sybil attack scenario in VANET and its consequences on the performance of the network. We also propose a lightweight, scalable and distributed detection approach based on the difference in movement patterns of Sybil nodes and legitimate nodes. In our approach, each Road Side Unit (RSU) computes, stores and verifies various parameter values including RSS, distance, angle of passing-by vehicles through passive overhearing process to detect Sybil attackers. The combination of different parameters makes our detection approach highly accurate. We validate our results on realistic traces obtained from a multi-agent microscopic traffic simulator (MMTS). Simulation results show the effectiveness of the proposed approach to locate Sybil nodes with a different number of network parameters.