IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v15y2013i1d10.1007_s10796-010-9252-2.html
   My bibliography  Save this article

Two-stage database intrusion detection by combining multiple evidence and belief update

Author

Listed:
  • Suvasini Panigrahi

    (KIIT University)

  • Shamik Sural

    (Indian Institute of Technology Kharagpur)

  • Arun K. Majumdar

    (Indian Institute of Technology Kharagpur)

Abstract

Insider threats have gained prominence and pose the most challenging threats to a database system. In this paper, we have proposed a new approach for detecting intrusive attacks in databases by fusion of information sources and use of belief update. In database intrusion detection, only intra-transactional features are not sufficient for detecting attackers within the organization as they are potentially familiar with the day-to-day work. Thus, the proposed system uses inter-transactional as well as intra-transactional features for intrusion detection. Moreover, we have also considered three different sensitivity levels of table attributes for keeping track of the malicious modification of the highly sensitive attributes more carefully. We have analyzed the performance of the proposed database intrusion detection system using stochastic models. Our system performs significantly better compared to two intrusion detection systems recently proposed in the literature.

Suggested Citation

  • Suvasini Panigrahi & Shamik Sural & Arun K. Majumdar, 2013. "Two-stage database intrusion detection by combining multiple evidence and belief update," Information Systems Frontiers, Springer, vol. 15(1), pages 35-53, March.
  • Handle: RePEc:spr:infosf:v:15:y:2013:i:1:d:10.1007_s10796-010-9252-2
    DOI: 10.1007/s10796-010-9252-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-010-9252-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10796-010-9252-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    References listed on IDEAS

    as
    1. Triantafyllopoulos, Kostas & Pikoulas, John, 2002. "Multivariate Bayesian Regression Applied to the Problem of Network Security," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(8), pages 579-594, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Carly L. Huth & David W. Chadwick & William R. Claycomb & Ilsun You, 2013. "Guest editorial: A brief overview of data leakage and insider threats," Information Systems Frontiers, Springer, vol. 15(1), pages 1-4, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Triantafyllopoulos, Kostas, 2006. "Multivariate discount weighted regression and local level models," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3702-3720, August.
    2. Godolphin, E.J. & Triantafyllopoulos, Kostas, 2006. "Decomposition of time series models in state-space form," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2232-2246, May.
    3. K. Triantafyllopoulos, 2007. "Covariance estimation for multivariate conditionally Gaussian dynamic linear models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(8), pages 551-569.
    4. K. Triantafyllopoulos, 2008. "Multivariate stochastic volatility with Bayesian dynamic linear models," Papers 0802.0214, arXiv.org.

    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:spr:infosf:v:15:y:2013:i:1:d:10.1007_s10796-010-9252-2. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.