An Efficient Approach for Detection and Prevention of Attacks in VANETs

VANETs are enabling the advancement of Internet of Vehicles. Network integrity is in great danger from Wormhole Attack (WHA), Black-Hole Attack (BHA), and GrayHole Attack (GHA). To overcome these threats, this research proposes a new security technique called Detection and Prevention of Gray-Hole, Black-Hole, and Wormhole Attacks. In the proposed system, a novel Distributed Detection and Prevention System (DP-GBW), specifically designed for Ad Hoc On-Demand Distance Vector (AODV)-based VANETs, is employed. These attacks may lower the VANETs’ security level, and utilizing this proactive detection and prevention method, ensure a higher level of security. The proposed technique fulfills its intended goal of resisting malicious node attacks while at the same time ensuring that trust values for regular nodes are calculated accurately based on the actual operating environment as supported by simulation experiments. This method enhances the security and reliability of the VANET making the vehicular communication network work together more securely.