IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v61y2016i3d10.1007_s11235-015-0017-6.html
   My bibliography  Save this article

An unsupervised approach for traffic trace sanitization based on the entropy spaces

Author

Listed:
  • Pablo Velarde-Alvarado

    (Autonomous University of Nayarit)

  • Cesar Vargas-Rosales

    (Tecnológico de Monterrey)

  • Rafael Martinez-Pelaez

    (Autonomous University of Ciudad Juarez)

  • Homero Toral-Cruz

    (University of Quintana Roo)

  • Alberto F. Martinez-Herrera

    (Tecnológico de Monterrey)

Abstract

The accuracy and reliability of an anomaly-based network intrusion detection system are dependent on the quality of data used to build a normal behavior profile. However, obtaining these datasets is not trivial due to privacy, obsolescence, and suitability issues. This paper presents an approach to traffic trace sanitization based on the identification of anomalous patterns in a three-dimensional entropy space of the flow traffic data captured from a campus network. Anomaly-free datasets are generated by filtering out attacks and traffic pieces that modify the typical position of centroids in the entropy space. Our analyses were performed on real life traffic traces and show that the sanitized datasets have homogeneity and consistency in terms of cluster centroids and probability distributions of the PCA-transformed entropy space.

Suggested Citation

  • Pablo Velarde-Alvarado & Cesar Vargas-Rosales & Rafael Martinez-Pelaez & Homero Toral-Cruz & Alberto F. Martinez-Herrera, 2016. "An unsupervised approach for traffic trace sanitization based on the entropy spaces," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 61(3), pages 609-626, March.
    • Handle: RePEc:spr:telsys:v:61:y:2016:i:3:d:10.1007_s11235-015-0017-6
    DOI: 10.1007/s11235-015-0017-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-015-0017-6
    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/s11235-015-0017-6?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.

    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:telsys:v:61:y:2016:i:3:d:10.1007_s11235-015-0017-6. 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: 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.