Vehicular Ad Hoc Networks (VANETs) have the capability of swapping every node of every individual while driving and traveling on the roadside. The VANET-connected vehicle can send and receive data such as requests for emergency assistance, current traffic conditions, etc. VANET assistance with a vehicle for communication purposes is desperately needed. The routing method has the characteristics of safe routing to repair the trust-based features on a specific node.When malicious activity is uncovered, intrusion detection systems (IDS) are crucial tools for mitigating the damage. Collaborations between vehicles in a VANET enhance detection precision by spreading information about interactions across their nodes. This makes the machine learning distribution system feasible, scalable, and usable for creating VANET-based cooperative detection techniques. Privacy considerations are a major impediment to collaborative learning due to the data flow between nodes. A malicious node can get private details about other nodes by observing them. This study proposes a cooperative IDS for VANETs that safeguards the data generated by machine learning. In the intrusion detection phase, the selected optimal characteristics is used to detect network intrusion via a hybrid Deep Neural Network and Bidirectional Long Short-Term Memory approach. The Trust-based routing protocol then performs the intrusion prevention process, stopping the hostile node by having it select the most efficient routing path possible.