Sybil Attack Prevention and Detection Mechanism in VANET Based on Multi-factor Authentication

In recent years, there has been fast development within the area of vehicular ad hoc networks (VANET). In the future, VANET communication will play a first-rate position in improving the protection and performance of the transportation system. If security isn't always furnished in VANET, then it may result in apparent misapplication. One of the dangerous or risky attacks in VANETs is the Sybil, which forges fake identities inside the network to disrupt or compromise the communication among the network nodes. Sybil attacks have an effect on the carrier transport associated with road safety, traffic congestion, multimedia entertainment and others. Thus, VANETs claim for a security mechanism to prevent Sybil attacks. Within this context, this paper proposes a mechanism, known as Sybil Attack Prevention and Detection Mechanism in VANET based on Multi-Factor Authentication (SAPDMV), to detect Sybil attacks in VANETs based on Multi-Factor Authentication. The proposed system works based on the principle of registration, and use identification number, status, Maximum and minimal threshold value and security key for the verification. The paper proposes a Sybil Attack Prevention and Detection Mechanism in VANET (SAPDMV) based on multifactor authentication. The mechanism uses vehicle identification, status, security key, and both minimum and maximum speed thresholds to authenticate nodes and detect Sybil attacks. Implemented and tested using Network Simulator-2.35, the system demonstrates an improved detection rate, reduced false positive and false negative rates, and enhanced network performance metrics such as end-to-end delay, throughput, and packet delivery ratio. The simulation result shows our proposed algorithm enhances detection rate, false positive rate, and false negative rate. The proposed solution is improved to 96%, 5%, and 4%, respectively, compared with the Sybil attack-AODV and existing/old work. The approach is scalable and effective in real-world VANET environments, making it a promising framework for future intelligent transportation systems.