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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
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