Advanced Security for MANET using Ant Colony Optimization and Artificial Neural Network

Manets are taught as a base for infrastructure wireless networks. Today main topics of research in Manets rotate around two main aspects- lifetime and security. This is just because of the wireless nature wherein vulnerability into network becomes comparatively much easier and the energy efficiency of all the nodes again is a crucial issue. The recent research is focused at enhancing security of Manets using the Ant Colony Optimization through Artificial Neural Networks thereby securing Manets from several intrusion attacks like worm hole and black hole attacks. The majority of optimization techniques work in past has been done using GA and now, the most recent algorithms like Particle swarm optimization, Tabu Search, ACO etc. have majorly reduced the limitations like premature convergence, which GA suffered from. Here, we will use ACO through Artificial Neural networks to create a robust system to increase the overall security of Manets. ANNs are the computational networks which are based on the behavior of biological neurons. These networks change through the information transferring through the network and provides help in pattern matching of inputs thereby guiding towards the most optimized output.