Mitigating Mobile Adhoc Network (MANET) Security Using Sequential Patterned Neural Network Method

The Mobile Adhoc Network (MANET) is an autonomous network that can be adopted dynamically. There is no need of infrastructure as well as it has ability to access where the fixed configuration is not achievable. There are various routing protocol that has provided communication between the nodes in wireless network in which familiar and best routing protocol is Adhoc on-demand Distance Vector (AODV) protocol. Security is essential for all the devices in the network system in which MANET doesn't show any different. This research discuses about tackling various attacks of Denial-ofService (DoS) in MANET as well as demonstrates that a broad classification model may fail in identifying these types of attacks because it cannot distinguish among network failures and an actual DoS attack. Moreover, the working of Machine Learning (ML) goal is about identification of complication pattern involved in AODV routing protocol of MANET which made decision with respect to the acquired results. One of the challenging security tasks is securing the routing path over implementation of MANET environment with no infrastructure. Therefore, proposed Neural Network (NN) with sequential pattern using Artificial NN (ANN) in AODV routing over MANET environment has generated suitable security for various DoS attacks. Thus, the approach of security has mainly focus on mitigating MANET security by identify and avoiding malicious nodes to generate secured routing path over MANET environment. Hence, the security approaches in MANETs mainly focus on mitigating security, eliminate malicious node as well as secure routing paths.