A Multiple Stage Deep Learning Model for NID in MANETs

Abstract

A MANET is an entirely devoid-of-infrastructure network. This network is made up of nodes that randomly move around. Since MANET has no central supervision, it can be formed anywhere using randomly moving nodes. This network faces numerous security issues as a result of MANET's vulnerable behaviour. There are numerous security threats to MANET that do not have a solution. It is also difficult to detect these issues. Some security threats are extremely serious. These threats have the potential to bring the network to its knees. Researchers are attempting to determine how to respond to these threats. The NID system is an important tool for protecting MANETs from vulnerabilities and malicious activities. A slew of new techniques have recently been demonstrated; however, due to the continuous launch of the various threats that existing systems are unable to detect, these techniques face significant challenges. The authors have proposed two stage deep learning (TSDL) model in this publication. For efficient NID, a stacked auto-encoder (SAE) with a softmax classifier (SMC) is used. There are two decisive phases in the model: A first phase in the system traffic classification process that uses a possibility score value to determine whether system movement is regular or irregular. This is then used as a bonus feature during the last stage of the decision-making process. Both the normal state and various types of attacks are to be detected, the suggested framework can automatically and efficiently gain knowledge and categories of beneficial feature representations from large amounts of unlabelled data.