IDEAS home Printed from https://ideas.repec.org/a/igg/jisp00/v13y2019i4p28-52.html
   My bibliography  Save this article

A Framework for Various Attack Identification in MANET Using Multi-Granular Rough Set

Author

Listed:
  • N. Syed Siraj Ahmed

    (VIT University, Vellore, India)

  • Debi Prasanna Acharjya

    (School of Computing Sciences and Engineering,VIT University, Vellore, India)

Abstract

The topology changes randomly and dynamically in a mobile adhoc network (MANET). The composite characteristics of MANETs makes it exposed to interior and exterior attacks. Avoidance support techniques like authentication and encryption are appropriate to prevent attacks in MANETs. Thus, an authoritative intrusion detection model is required to prevent from attacks. These attacks can be at either the layers present in the network or can be of a general attack. Many models have been developed for the detection of intrusion and detection. These models aim at any one of the layer present in the network. Therefore, effort has been made to consider either the layers for the detection of intrusion and detection. This article uses a multigranular rough set (MGRS) for the detection of intrusion and detection in MANET. The advantage of MGRS is that it can aim at either the layers present in the network simultaneously by using multiple equivalence relations on the universe. The proposed model is compared with many traditional models and attained higher accuracy.

Suggested Citation

  • N. Syed Siraj Ahmed & Debi Prasanna Acharjya, 2019. "A Framework for Various Attack Identification in MANET Using Multi-Granular Rough Set," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 13(4), pages 28-52, October.
  • Handle: RePEc:igg:jisp00:v:13:y:2019:i:4:p:28-52
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJISP.2019100103
    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:igg:jisp00:v:13:y:2019:i:4:p:28-52. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.