IDEAS home Printed from https://ideas.repec.org/a/bjf/journl/v7y2022i2p70-75.html
   My bibliography  Save this article

Development of Machine Learning Based Security Algorithm for 4G Network against Wormhole

Author

Listed:
  • Eze E.M.

    (Enugu State University of Science and Technology)

  • Ituma C.

    (Enugu State University of Science and Technology)

  • Asogwa T.C

    (Enugu State University of Science and Technology)

  • Ebere U.C.

    (Destinet Smart Technologies Ltd.)

Abstract

TThis work development of machine learning based security algorithm for 4G network against wormhole. This was achieved using methods such as data collection, data extraction, training and classification process. The system design employed mathematical and structural method to develop the models of the wormhole and also the new security algorithm using artificial neural network. This was implemented with Simulink and neural network toolbox, before testing. The result showed that the algorithm was able to detect wormhole at a regression of 0.9978 and Mean square error of 2.05×10^-5. The security algorithm deployed on a 4G network and tested; the result showed that throughput percentage of 89.16% and latency of 76.325ms which according to International Telecommunication Union (ITU-U) and Nigerian Communication Commission standard (NCC) are good.

Suggested Citation

  • Eze E.M. & Ituma C. & Asogwa T.C & Ebere U.C., 2022. "Development of Machine Learning Based Security Algorithm for 4G Network against Wormhole," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 7(2), pages 70-75, February.
    • Handle: RePEc:bjf:journl:v:7:y:2022:i:2:p:70-75
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrias/digital-library/volume-7-issue-2/70-75.pdf
    Download Restriction: no
    File URL: https://www.rsisinternational.org/virtual-library/papers/development-of-machine-learning-based-security-algorithm-for-4g-network-against-wormhole/?utm_source=Netcore&utm_medium=Email&utm_content=sscollections25oct&utm_campaign=First&_gl=1*bgu4lf*_gcl_au*Nzg3MDc3MjYxLjE3MDIwMTAzMzE.*_ga*MTA1MTkzODcwMi4xNjk0MTkxNTI0*_ga_J3C1TKKSZ0*MTcwODA2NTE4MS4yNDkuMS4xNzA4MDY2Njg5LjYwLjAuMA..&_ga=2.98250847.1939239374.1707887915-1051938702.1694191524
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjf:journl:v:7:y:2022:i:2:p:70-75. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrias/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.