Enhanced Machine Learning Algorithms for Validating the Biometrics in VANET

In this rapidly sprouting technological era, trans-portation is also becoming intelligent and driverless. Ad hoc networking plays an essential role in making the vehicle to vehicle and vehicle to infrastructure communication environment compatible with all types of users. As technology grows, the risk of security and privacy also grows side by side. However, the intruders can easily find out holes in the security network and gather information from them. The scope of the paper is to propose a methodology that inflates the security of stored data in the hardware of vehicular networks and identity verification of vehicles in communication easier. The importance of the present research is that these proposed simulation techniques help in maintaining the security of temper proof devices used in VANETs for the storage of private data. Authors communicate all sensitive data like bank details, vehicle details and personal details over wireless networks during communication. Biometric data and many other cryptographic techniques are used to secure the communication. Proposed methodologies are very efficient, less time consuming and less cost effective too which can be used in any type of intelligent vehicle with no extra hardware requirement.