A preventive approach to mitigate the effects of gray hole attack using genetic algorithm

VANET (Vehicular Ad-Hoc Network) is an optimistic approach to present safety and other applications to the clients and it is the key module of the intelligent transport system. In this paper, Grayhole attack is mitigated with respect to VANET. The prevention is done through Genetic Algorithm. Genetic Algorithm produce solutions to optimization problems by means of techniques stimulated by natural development like inheritance, mutation, selection, and crossover. Two parameters of the fitness function are passed that contain the feature set of one node and threshold for the network. Genetic Algorithm will mitigate the effects. Further, the optimization is done through Artificial Bee colony (ABC) Algorithm. It is developed on the basis of inspecting the behaviors of real bees on judging nectar and distributing the information of food sources for bees in hive. The experimental results show that the application of Genetic Algorithm in mitigation of Grayhole attack outperforms Artificial Bee Colony algorithm. The performance of the network has been evaluated using throughput, end to end delay, packet delivery ratio, bit error rate.