Study on Intelligent Hierarchical Security Framework for VANETS

Vehicles are being added to new vehicular ad hoc networks (VANETs). They create “Intelligent Transportation Systems (ITS)” & platforms that enable interaction among road entities, encourage the development of innovative applications, as well as promote traffic safety services. Attackers can take advantage of the difficult security setup of VANETs. Strong authentication, entity interaction privacy, and anonymity are all emphasized in this project. A V ANET Machine Learning (ML) algorithm-based Intelligent Hierarchical Security Framework for threat identification is the secondary objective. Both are accomplished by this effort. Public vehicle communications datasets are used by this system to train and verify machine learning algorithms. These datasets were used to train and test the ML algorithms in this framework. Assaults can be found by the “proposed Intrusion Detection System (IDS)” even when the FPR is low. Numerous hierarchical algorithms may be advantageous to the framework, according to the statistics. At lower levels, quick and light processing might be advantageous, but accuracy would suffer. Higher-level hierarchical nodes require algorithms that are more precise, accurate, and advanced