International Journal of Science, Engineering and TechnologyAuthors: Kashifa Khan, Associate Professor Devdas Saraswat
Abstract: Vehicular Ad hoc Network (VANETs) face numerous issues due to the free movement of vehicles within the network. These problems include high bandwidth consumption, as vehicles often wait for responses and properly received traffic information from one another. The innovative RSML system enhances communication speed but requires additional security features to identify and shield the VANET from blackhole attacks. RSML incorporates machine learning techniques within roadside units (RSUs) to safeguard the transportation network. In the context of routing, blackhole attackers seek to disrupt normal routing functions. These attackers consistently generate high sequence numbers to participate in the routing process. The RSML method utilizes machine learning to effectively detect and mitigate black hole threats in vehicular ad hoc networks. By employing advanced algorithms, the RSML method can adapt to changing attack patterns, ensuring strong protection for vehicle communications. Establishing a route in vehicular communication is challenging because a malicious vehicle can exploit this process for its advantage. The RSML approach maintains the record of attacker malicious functioning and match the malicious functions. Therefore, it is essential to select a routing technique that guarantees secure communication. The performance of RSML is compared with existing balckhole attack, T-AODV and ANN-AODV. The performance of novel RSML scheme is better.
