IDEAS home Printed from https://ideas.repec.org/a/igg/jamc00/v12y2021i2p36-48.html
   My bibliography  Save this article

Role-Based Profiling Using Fuzzy Adaptive Resonance Theory for Securing Database Systems

Author

Listed:
  • Anitarani Brahma

    (Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Burla, India)

  • Suvasini Panigrahi

    (Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Burla, India)

Abstract

Very large amounts of time and effort have been invested by the research community working on database security to achieve high assurance of security and privacy. An important component of a secure database system is intrusion detection system which has the ability to successfully detect anomalous behavior caused by applications and users. However, modeling the normal behavior of a large number of users in a huge organization is quite infeasible and inefficient. The main purpose of this research investigation is thus to model the behavior of roles instead of users by applying adaptive resonance theory neural network. The observed behavior which deviates from any of the established role profiles is treated as malicious. The proposed model has the advantage of identifying insider threat and is applicable for large organizations as it is based on role profiling instead of user profiling. The proposed system is capable of detecting intrusion with high accuracy along with minimized false alarms.

Suggested Citation

  • Anitarani Brahma & Suvasini Panigrahi, 2021. "Role-Based Profiling Using Fuzzy Adaptive Resonance Theory for Securing Database Systems," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 12(2), pages 36-48, April.
  • Handle: RePEc:igg:jamc00:v:12:y:2021:i:2:p:36-48
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJAMC.2021040103
    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:jamc00:v:12:y:2021:i:2:p:36-48. 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.