IDEAS home Printed from https://ideas.repec.org/a/epw/ejece0/v10y2026i1id70102.html

Applying Reinforcement Learning to Cyber Security: A Performance Comparison Using an IoT Security Dataset

Author

Listed:
  • Md Rakibul Karim Akanda

    (Savannah State University, United States of America)

  • Jinorri Jovel Wilson

    (Savannah State University, United States of America)

Abstract

This paper shows the fundamental principles of reinforcement learning (RL) and its application in cyber security. We simulated reinforcement learning by utilizing an IoT (Internet of Things) blockchain security dataset. The paper shows the difference in performance of reinforcement learning compared to random forest classifier machine learning models. Comparison of these two different machine learning models shows the prospects of RL in cyber defense strategies.

Suggested Citation

  • Md Rakibul Karim Akanda & Jinorri Jovel Wilson, 2026. "Applying Reinforcement Learning to Cyber Security: A Performance Comparison Using an IoT Security Dataset," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 10(1), pages 37-40, January.
    • Handle: RePEc:epw:ejece0:v:10:y:2026:i:1:id:70102
    DOI: 10.24018/ejece.2026.10.1.70102
    as

    Download full text from publisher

    File URL: https://eu-opensci.org/index.php/ejece/article/view/70102
    File Function: Abstract page
    Download Restriction: no
    File URL: https://eu-opensci.org/index.php/ejece/article/download/70102/13700
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.24018/ejece.2026.10.1.70102?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
    ---><---

    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:epw:ejece0:v:10:y:2026:i:1:id:70102. 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: support (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejece .

    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.