Mitigating Intruder Detection System in Mobile Adhoc Network (MANET) using optimizer based ANN model

Abstract

Mobile Adhoc Network (MANET) is an adoptable network with dynamic as well as decentralized network in nature. This research concentrates to resolve Denial-of-Service (DoS) attacks in MANET and illustrates the usual classification models, which may be unable to distinguish between legitimate DoS attacks as well as network problems. The routing path has been recognized as well as strengthen the environmental adoption with respect to several logic and statistical performances using Machine Learning (ML). The main aim in ML for recognizing the complexity pattern with AODV routing in MANET as well as decision making in accordance with accomplished result. In securing the MANET, there are several ML algorithms have been implemented. The lack of infrastructure in MANETs makes it extremely difficult to implement security measures. Moreover, the proposed sequential pattern with Artificial Neural Network (ANN) for AODV routing has generated better security from DoS attack. Thus, the security methods in MANETs majorly concentrating in mitigating intruder detection, eliminating malicious node as well as securing routing paths.