IDEAS home Printed from https://ideas.repec.org/a/igg/jcini0/v14y2020i1p17-34.html
   My bibliography  Save this article

Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning

Author

Listed:
  • Maryam Ghanbari

    (University of Manitoba, Winnipeg, Canada)

  • Witold Kinsner

    (University of Manitoba, Winnipeg, Canada)

Abstract

Distributed denial-of-service (DDoS) attacks are serious threats to the availability of a smart grid infrastructure services because they can cause massive blackouts. This study describes an anomaly detection method for improving the detection rate of a DDoS attack in a smart grid. This improvement was achieved by increasing the classification of the training and testing phases in a convolutional neural network (CNN). A full version of the variance fractal dimension trajectory (VFDTv2) was used to extract inherent features from the stochastic fractal input data. A discrete wavelet transform (DWT) was applied to the input data and the VFDTv2 to extract significant distinguishing features during data pre-processing. A support vector machine (SVM) was used for data post-processing. The implementation detected the DDoS attack with 87.35% accuracy.

Suggested Citation

  • Maryam Ghanbari & Witold Kinsner, 2020. "Detecting DDoS Attacks Using Polyscale Analysis and Deep Learning," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 14(1), pages 17-34, January.
  • Handle: RePEc:igg:jcini0:v:14:y:2020:i:1:p:17-34
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCINI.2020010102
    Download Restriction: no
    ---><---

    More about this item

    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:igg:jcini0:v:14:y:2020:i:1:p:17-34. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.