IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i10p1439-d1389937.html
   My bibliography  Save this article

GAN-Based Anomaly Detection Tailored for Classifiers

Author

Listed:
  • Ľubomír Králik

    (Department of Communication Networks, Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia)

  • Martin Kontšek

    (Department of Communication Networks, Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia)

  • Ondrej Škvarek

    (Department of Communication Networks, Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia)

  • Martin Klimo

    (Department of Communication Networks, Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia)

Abstract

Pattern recognition systems always misclassify anomalies, which can be dangerous for uninformed users. Therefore, anomalies must be filtered out from each classification. The main challenge for the anomaly filter design is the huge number of possible anomaly samples compared with the number of samples in the training set. Tailoring the filter for the given classifier is just the first step in this reduction. Paper tests the hypothesis that the filter trained in avoiding “near” anomalies will also refuse the “far” anomalies, and the anomaly detector is then just a classifier distinguishing between “far real” and “near anomaly” samples. As a “far real” samples generator was used, a Generative Adversarial Network (GAN) fake generator that transforms normally distributed random seeds into fakes similar to the training samples. The paper proves the assumption that seeds unused in fake training will generate anomalies. These seeds are distinguished according to their Chebyshev norms. While the fakes have seeds within the hypersphere with a given radius, the near anomalies have seeds within the sphere near cover. Experiments with various anomaly test sets have shown that GAN-based anomaly detectors create a reliable anti-anomaly shield using the abovementioned assumptions. The proposed anomaly detector is tailored to the given classifier, but its limitation is due to the need for the availability of the database on which the classifier was trained.

Suggested Citation

  • Ľubomír Králik & Martin Kontšek & Ondrej Škvarek & Martin Klimo, 2024. "GAN-Based Anomaly Detection Tailored for Classifiers," Mathematics, MDPI, vol. 12(10), pages 1-22, May.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1439-:d:1389937
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/10/1439/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/10/1439/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. St'ephane Cr'epey & Lehdili Noureddine & Nisrine Madhar & Maud Thomas, 2022. "Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural Networks," Papers 2209.11686, arXiv.org, revised Oct 2022.
    2. Nuray Tosunoğlu & Hilal Abacı & Gizem Ateş & Neslihan Saygılı Akkaya, 2023. "Artificial neural network analysis of the day of the week anomaly in cryptocurrencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-24, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuqi Nie & Yaxuan Kong & Xiaowen Dong & John M. Mulvey & H. Vincent Poor & Qingsong Wen & Stefan Zohren, 2024. "A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges," Papers 2406.11903, arXiv.org.
    2. Zeliha Can Ergün, 2024. "Calendar Anomalies in NFT Coins," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 9(1), pages 43-60.

    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:gam:jmathe:v:12:y:2024:i:10:p:1439-:d:1389937. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.