Sybil Assault Detection in VANET with Efficient Network Analysis

A developing technology that can be highly helpful for both intelligent transportation services and safety and security is the Automobile ad hoc network, or VANET. Nevertheless because of the great mobility and wireless connection of cars, there are numerous security risks that jeopardize people's safety and security when driving. The Sybil assault, in which the perpetrator makes up a false identity to obtain significant influence within the network and delay certain services and manipulate others by fictitious voting, is one of the main security issues. People can avoid being duped by an attacker or falling into traps when attacks are detected early.. This study describes two methods for detecting Sybil attacks: the first uses the Poisson Distribution Method in order to forecast traffic on the street, whereas the latter examines network efficiency for packet delivery ratio (PDR) in both benign additionally malicious environments. According to the simulation's findings, PDR lowers the number of fictitious cars in the network. Our method is straightforward and efficient because it doesn't involve a lot of computational overhead and doesn't breach users' privacy concerns.