Enhanced Security Framework for Gray-Hole Attack Prevention in Vehicular Networks

The gray-hole attacks are certainly destructive to the integrity and reliability of vehicular ad-hoc networks where a malicious node will drop packets selectively while keeping the instance innocuous. Commercial and Safety Applications Vehicular Ad-Hoc Networks and IoT of Vehicles, ITS, and V2X VANETs nowadays are advancing fast in tune with the commercial and safety applications. Nonetheless, despite offering plenty of possible advantages, VANETs cowl an openly-distributed-dynamic nature that raises their exposure to different classes of attacks and threats, one of those being the gray-hole attack (GHA)_. The first version is known as Smart GHA that did not disclose its wrongdoing during routing discoveries, whereas in the second variant-a sequence-based GHA-it behaves wrongly at the route discovery stage but selectively drops packets-after the route was established-in this two-pronged form. The present work specifies the novel strategy in the field of security which is termed Detection and Prevention of GHA (DPGHA). In it, both forms of GHA have been detected and prevented in AODV-based VANETs. The dynamic threshold values defined in DPGHA are based on the anomalies in the packets that are received, forwarded, and generated with their sequence numbers. Dynamic threshold values and dynamic monitoring could increase computational complexity even more and increase the processing requirements imposed on a node with limited resources. The algorithm endorses cooperative detection of malicious behavior and vehicles' resilience. Simulation and evaluation of the ARBD algorithm indicate enhanced accuracy and efficiency in the identification of gray-hole attackers, thus securing and boosting VANET performance.