Mitigating DDoS attacks in VANETs using a Variant Artificial Bee Colony Algorithm based on cellular automata

Artificial Bee Colony Optimization Algorithm (ABCA) is a powerful optimization scheme that is suitable for a number of complex applications in which iteratively the best solution is to be created from the viable candidate solution. This ABCA applicability can be used as an ad hoc vehicle for minimizing DDoS attacks. A Variant Artificial Bee Colony Algorithm (VABCA) is available in this paper for optimizing the selection of a vehicle node for substitution of the damaged DDoS vehicle node. VABCA is an improved ABCA version which uses two search strategies based on differential evolution in the onlooker bee and an integrated Chaotic and opposition learning in scout bee. The principal goal of VABCA is to increase the global optimum detection point in DDoS attacks and to have a good degree of convergence rate and efficiency in order to distinguish the best solutions from the workable solutions. The VABCA simulation findings show that DDoS mitigation is potent by encouraging an approximately 22% rate higher in convergence than in the comparative research baseline mitigation schemes.