IDEAS home Printed from https://ideas.repec.org/a/wsi/jikmxx/v07y2008i03ns0219649208002044.html
   My bibliography  Save this article

Classification Methods in the Detection of New Suspicious Emails

Author

Listed:
  • S. Appavu Alias Balamurugan

    (Department of Information Technology, Thiagarajar College of Engineering, Madurai-625015, India)

  • G. Athiappan

    (Department of Information Technology, Thiagarajar College of Engineering, Madurai-625015, India)

  • M. Muthu Pandian

    (Department of Information Technology, Thiagarajar College of Engineering, Madurai-625015, India)

  • R. Rajaram

    (Department of Computer Science & Engineering, Thiagarajar College of Engineering, Madurai-625015, India)

Abstract

Email has become one of the fastest and most economical forms of communication. However, the increase of email users has resulted in the dramatic increase of suspicious emails during the past few years. This paper proposes to apply classification data mining for the task of suspicious email detection based on deception theory. In this paper, email data was classified using four different classifiers (Neural Network, SVM, Naïve Bayesian and Decision Tree). The experiment was performed using weka on the basis of different data size by which the suspicious emails are detected from the email corpus. Experimental results show that simple ID3 classifier which make a binary tree, will give a promising detection rates.

Suggested Citation

  • S. Appavu Alias Balamurugan & G. Athiappan & M. Muthu Pandian & R. Rajaram, 2008. "Classification Methods in the Detection of New Suspicious Emails," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 7(03), pages 209-217.
    • Handle: RePEc:wsi:jikmxx:v:07:y:2008:i:03:n:s0219649208002044
    DOI: 10.1142/S0219649208002044
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219649208002044
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219649208002044?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:wsi:jikmxx:v:07:y:2008:i:03:n:s0219649208002044. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/jikm/jikm.shtml .

    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.