IDEAS home Printed from https://ideas.repec.org/a/ids/ijidsc/v18y2026i1p60-70.html

A classification model for terror incidents by affiliation category of perpetrators

Author

Listed:
  • Donald Douglas Atsa'am
  • Benjamin Terzungwe Tough
  • Barr. Doose Atsa'am

Abstract

A terror incident could be perpetrated by either a lone wolf who acts on their own or affiliated terrorists who work for a terror group. In this study, the data from the global terrorism database and the artificial neural network algorithm were employed to construct a classification model that could predict the probable affiliation category of the perpetrator(s) of a terror incident. The model uses information such as type of attack, casualty figure, claim of responsibility, and damage to property to distinguish a lone wolf attack from a terror group attack. Various metrics of model diagnostics were employed to test the suitability of the model for predictions, and it yielded a balanced classification accuracy of 85%. The model adds another dimension to the existing criteria for terrorism classification. Further, the model could serve as a useful tool in the study of terrorism and counterterrorism.

Suggested Citation

  • Donald Douglas Atsa'am & Benjamin Terzungwe Tough & Barr. Doose Atsa'am, 2026. "A classification model for terror incidents by affiliation category of perpetrators," International Journal of Information and Decision Sciences, Inderscience Enterprises Ltd, vol. 18(1), pages 60-70.
    • Handle: RePEc:ids:ijidsc:v:18:y:2026:i:1:p:60-70
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=152462
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:ids:ijidsc:v:18:y:2026:i:1:p:60-70. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=306 .

    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.