Detection of Black Hole Attack In Mobile AD-HOC Networks Using Hybrid Glowworm Swarm Optimization (HGSO) Algorithm with Artificial Fish Swarm Algorithm (AFSA)

A mobile ad-hoc network is a group of mobile nodes which can communicate between them without the help of any centralized infrastructure. It consists of number of mobile nodes with exceptional quality of self-managing and selforganizing network. Military operations and disaster management are the important applications of MANET. In preventing routing attacks routing protocols plays a significant role. Security attacks can be initiated towards any layer of the stack protocol. MANET’S distinctive characteristic like dynamic network topology, battery power and limited bandwidth makes routing a challenging task. Several researches are done in this area and many efficient routing protocols were proposed. But due to the presence of malicious node, these protocols are vulnerable to attacks. Therefore for establishing the attractive MANET, security is a major concern. In this work, hybrid swarm intelligence algorithms are employed to overcome the drawbacks of the previous approaches. Here, proposed a Hybrid Glowworm Swarm Optimization (HGSO) algorithm with Artificial Fish Swarm Algorithm (AFSA). This hybrid algorithm has immediate convergence and searching potential to solve the routing complication effectively. This modified protocol is compared with existing protocol by using various parameters i.e. packet delivery ratio, end-to-end delay and throughput. The results shows to increase in packet delivery ratio, throughput and decrease in end-to end delay show better performance of proposed work as compared existing.