An Efficient Neuro Deep Learning Intrusion Detection System for Mobile Adhoc Networks

N.  Venkateswaran; S. Prabaharan Prabaharan

doi:10.4108/eai.4-4-2022.173781

Authors

N. Venkateswaran Jyothishmathi Institute of Technology and Science
S. Prabaharan Prabaharan Mallareddy Institute of Engineering and Technology

DOI:

https://doi.org/10.4108/eai.4-4-2022.173781

Keywords:

Deep Learning, Intrusion Detection, Mobile Adhoc Networks, MANET, Deep Neural Network, recurrent neural networks, intrusion detection systems, IDS

Abstract

As of late mobile ad hoc networks (MANETs) have turned into a very popular explore the theme. By giving interchanges without a fixed infrastructure MANETs are an appealing innovation for some applications, for ex, reassigning tasks, strategic activities, nature observing, meetings, & so forth. This paper proposes the use of a neuro Deep learning wireless intrusion detection system that distinguishes the attacks in MANETs. Executing security is a hard task in MANET due to its immutable vulnerabilities. Deep learning gives extra security to such systems and the proposed framework comprises a hybrid conspiracy that joins the determination and abnormality-based methodologies. Executing the partial IDS utilizing neuro Deep learning improves the identification rate in MANETs. The proposed plan utilizes deep neural networks and a cross breed neural system. It demonstrates that Recurrent neural networks can successfully improve the identification and diminish the rate of false caution and failure.

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