IDEAS home Printed from https://ideas.repec.org/a/vra/journl/y2016i2p117-123.html
   My bibliography  Save this article

Finding a Spam Email Messages Using Data Mining Methods

Author

Listed:
  • Snezhana Sulova

    (University of Economics - Varna)

Abstract

There are many software solutions that have been developed based on the use of various software technologies for identification of e-mail spam messages. This article presents how we may successfully use data mining methods for identifying spam messages. The proposed approach is based on Supervised Machine Learning methods - Support Vector Machines (SVM) and Naive Bayes (NB). Exemplary model for email messages extraction and classification is implemented in RapidMiner.

Suggested Citation

  • Snezhana Sulova, 2016. "Finding a Spam Email Messages Using Data Mining Methods," Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series, Union of Scientists - Varna, Economic Sciences Section, issue 2, pages 117-123, November.
    • Handle: RePEc:vra:journl:y:2016:i:2:p:117-123
    as

    Download full text from publisher

    File URL: http://www.su-varna.org/izdanij/2016/ikonom-2-016/p%20117-123.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Data Mining; Web Mining; classification; Support Vector Machines; Naive Bayes; Internet; e-mail; spam; RapidMiner;
    All these keywords.

    JEL classification:

    • A00 - General Economics and Teaching - - General - - - General

    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:vra:journl:y:2016:i:2:p:117-123. 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: Pavel Petrov (email available below). General contact details of provider: https://edirc.repec.org/data/uevecea.html .

    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.