Design a Process to Detect and Mitigate the Malicious Attacks in VANET Using Ml Approach

Abstract

The research intends to detect suspicious behavior in VANETs and block the vehicles involved from using the network for its intended purpose of safe information exchange. To enhance traffic safety and efficiency, VANETs use wireless ad hoc communication between vehicles and roadside devices to share cooperative awareness data and event-based messaging. By considering the existence and condition of cars traveling within a set range, drivers can receive quick notifications about potentially hazardous scenarios, such as a vehicle suddenly braking in front of the back of traffic congestion up ahead or the hacking of shared information inside a network. Within a communication range of a few hundred meters, nodes in a VANET will often broadcast mobility-related data (i.e. absolute values for position, time, heading, and speed) to develop a communal knowledge of single-hop neighbors. The ability for network nodes to communicate ad hoc allows traffic safety software to run with little lag.

For the purpose of protecting VANETs from outside attackers, the suggested HCMNDA (Hybrid cooperative malicious node detection Approach) methods employ automated prediction. Only VANET-registered nodes have addresses that have been validated by a trusted certificate authority. Internal attackers with the right hardware, software, and valid certificates can be detected by analyzing stored data in a table using a clustering approach. I illustrate how the Attacker's single-hop or multi-hop communication range may be negatively affected by the processing of incorrect information on the traffic's overall security and performance. The majority of existing VANET misbehavior detection systems focus on data-centric plausibility and consistency criteria.

Most existing solutions are only tested via simulations. To the contrary, I employed a network-wide automated prediction of malicious activity to test how well VANET misbehavior detection stands up in the actual world. Long-term research in operational tests based on simulation, and dedicated trials employing test vehicles, yielded fresh insights that are presented here. Based on these findings, the cutting-edge HCMNDA (Hybrid cooperative malicious node detection Approach) method was developed. There are two main benefits to this approach: the ability to quickly pinpoint potential attackers within the cluster and the ability to spot irregular behavior in the immediate vicinity. Using this strategy, which can detect abnormal node behavior should be effective even against yet-to-be-developed attack methods.