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sis 22(6): e7

Research Article

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

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  • @ARTICLE{10.4108/eai.4-4-2022.173781,
        author={N. Venkateswaran and S. Prabaharan Prabaharan},
        title={An Efficient Neuro Deep Learning Intrusion Detection System for Mobile Adhoc Networks},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={9},
        number={6},
        publisher={EAI},
        journal_a={SIS},
        year={2022},
        month={4},
        keywords={Deep Learning, Intrusion Detection, Mobile Adhoc Networks, MANET, Deep Neural Network, recurrent neural networks, intrusion detection systems, IDS},
        doi={10.4108/eai.4-4-2022.173781}
    }
    
  • N. Venkateswaran
    S. Prabaharan Prabaharan
    Year: 2022
    An Efficient Neuro Deep Learning Intrusion Detection System for Mobile Adhoc Networks
    SIS
    EAI
    DOI: 10.4108/eai.4-4-2022.173781
N. Venkateswaran1,*, S. Prabaharan Prabaharan2
  • 1: Jyothishmathi Institute of Technology and Science
  • 2: Mallareddy Institute of Engineering and Technology
*Contact email: venkywn@gmail.com

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.

Keywords
Deep Learning, Intrusion Detection, Mobile Adhoc Networks, MANET, Deep Neural Network, recurrent neural networks, intrusion detection systems, IDS
Received
2022-02-21
Accepted
2022-03-31
Published
2022-04-04
Publisher
EAI
http://dx.doi.org/10.4108/eai.4-4-2022.173781

Copyright © 2022 N. Venkateswaran et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

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