IDEAS home Printed from https://ideas.repec.org/a/ids/ijdmmm/v16y2024i3p293-325.html
   My bibliography  Save this article

Improving intrusion detection in the IoT with African vultures optimisation algorithm-based feature selection

Author

Listed:
  • Mohammed Alweshah
  • Ghadeer Ahmad Alhebaishan
  • Sofian Kassaymeh
  • Saleh Alkhalaileh
  • Mohammed Ababneh

Abstract

The security of the system may be jeopardised by unsecured data transmitted through IoT devices, and ensuring the reliability of data is critical to maintaining the integrity of information over the internet. To enhance the intrusion detection rate, several investigations have been conducted to develop methodologies capable of identifying the minimum required secure features. One such method is the use of the feature selection procedure with metaheuristic algorithms. In this study, the African vulture optimisation algorithm was used in two wrapper FS approaches to select the most secure features in IoT. The first approach used AVO, while the second employed OBL-AVO, a hybrid model combining AVO with opposition-based learning (OBL) to enhance exploration. Based on the outcomes, it was found that the OBL-AVO is superior to the AVO in enhancing FS. Furthermore, the proposed methods' were evaluated and compared to four recent approaches.

Suggested Citation

  • Mohammed Alweshah & Ghadeer Ahmad Alhebaishan & Sofian Kassaymeh & Saleh Alkhalaileh & Mohammed Ababneh, 2024. "Improving intrusion detection in the IoT with African vultures optimisation algorithm-based feature selection," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 16(3), pages 293-325.
  • Handle: RePEc:ids:ijdmmm:v:16:y:2024:i:3:p:293-325
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=140529
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijdmmm:v:16:y:2024:i:3:p:293-325. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=342 .

    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.